CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation . In elaborately designed experiments , CL-CIS is evaluated together with general causality analysis system ( GCAS ) and general causality analysis system with learning ( GCAS-L ) , and it testified to its own capability and performance in construction of cause-effect relations . This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning . This paper discusses a framework for describing and analysing how young students ( 5–6 years ) learn in technology with a view towards enhancing teaching and learning practice in technology . Examples of student work which demonstrate the complexity of learning in technology , and what young children can achieve with appropriate teaching strategies are presented . Holistic aspects as well as associated variables are highlighted . This gate , working with max - pooling and positive activation function ( e.g. , Sigmoid ) , keeps away the artifacts from padding in all layers . Actually it creates a natural hierarchy of all - zero padding ( as illustrated in Figure [ reference ] ) , consisting of nodes in the neural net that would not contribute in the forward process ( as in prediction ) and backward propagation ( as in learning ) . subsection : Some Analysis on the Convolutional Architecture the form of computation required to complete the task . In this work , we propose a meta - learning algorithm that is general and model - agnostic , in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure . Our focus is on deep neural network models , but we illustrate how our approach can easily handle different architectures and different problem settings , including classification , regression , and policy gradient reinforcement learning , with minimal modification . However , we have observed that we obtain substantially better results by “ inpainting ” the values of the supervision signal on positions that were not originally annotated . For this we adopt a learning - based approach where we firstly train a “ teacher ” network ( depicted in Fig . [ reference ] ) to reconstruct the ground - truth values wherever these are observed , and then deploy it on the full image domain , yielding a dense supervision signal . By virtue of its generality , the SPL theory has been widely applied to various tasks , such as multi - view clustering [ reference ] , multi - label propagation [ reference ] , multimedia event detection , and co - saliency detection . More recently , introduced the pre - defined learning curriculum to the conventional self - paced learning regime which can take into account both the helpful prior knowledge known before training and the self - learning progress during training . Inspired by this work , we design a task - specific learning curriculum and construct a unified SPCL model specifically for both the saliency detection and WOD tasks , through which both can be naturally related . In advanced automatic systems , control algorithms are based on fuzzy logic methods and using of artificial neural networks . The application of the developed control actions shaper does not require complicated adjustment of the regulator parameters by means of expert assessments or the learning process . Analysis of scientific literature and publications revealed the absence of standard program libraries for programming such shapers on microcontrollers or programmable logic controllers . The study was conducted in two cycles , and each cycle consists of two meeting . The data of this study is the action of the teacher in teaching , learning materials , students activity in learning learning process , and the result of student learning in writing the story . Data collection techniques used in this study were observation , interviews , field notes , questionnaires , and test performance . One of the most LMS in Indonesia is moodle , which is an open source LMS platform that can be developed independently . This study uses features to find features that can be used in the learning and learning process on SNS SN . The results of the features that will be implemented into the moodle system use system development methods , namely component-based software engineering ( CBSE ) , CBSE has several stages of development namely domain engineering , component engineering , system and software design , configuration and configuration , and testing and validation . We propose a learning process based approach to address the well-known implicit profiling method in group learning recommender system . Our approach makes use of likelihood control to integrate the learning step coefficient of each learner into ratings . From fluffy clouds to a meaningful learning process Learning is increasingly distributed over virtual , simulation and real-life environments . Collaboration takes place via cloud-services and social media chosen by the teacher , or by the students themselves . The technique chosen for the learning process was the Map of Kohonen , because it is a classic technique with a self-learning capacity . The solution investigated was part of a huge problem : the learning process of tasks by articulated robots , for example the pick-and-place task . The results that were found indicated that the proposed solution was able to learn in two scenarios : data of movements of the end effector in line and data of end effector realizing movements in a plane . subsection : Learning The learning goal is to maximize the estimated probability of the actual summary . We adopt the common negative log - likelihood ( NLL ) as the loss function . [ reference ] ) . From a learning perspective , the representation of the source sequence in the ByteNet is resolution preserving ; the representation sidesteps the need for memorization and allows for maximal bandwidth between encoder and decoder . In addition , the distance traversed by forward and backward signals between any input and output tokens corresponds to the fixed depth of the networks and is largely independent of the distance between the tokens . ASAC gradually reduces a parameter called an entropy temperature , which regulates the entropy of the control policy . In this way , the proposed method can narrow down the search space during training , and reduce the duration of demanding data collection processes in real-world experiments . For the verification of the robustness and the controllability of our robot and the RL algorithm , experiments for zig-zagging path tracking and obstacle avoidance were conducted , and the robot successfully finished the missions with only an hour of training time . The proposed scheme is built based on feed forward neural network model with multi-hidden layers . The model consists of four phases : data acquisition , pre-processing , feature extraction and neural network training and testing . In this study the neural network model provides higher forecast accuracy than other proposed methods . For further comparison , this work also compares Support Vector Machines to K-nearest neighbours , known for its simplicity and solid results in other domains . The third part of this dissertation investigates the automatic generation of training data . Classifier accuracy depends on good quality training data -- the wider the training data spectrum , the higher the classifier 's accuracy . For training National Guard units , the U.S. Army National Guard will field 21 combat vehicle training simulators called mobile trainers . Each National Guard unit must train at a station that is not farther than a specified maximum travel distance from its armory . We propose to employ a simple but effective strategy called “ mixup ” to overcome the gap in feature-target and target-target covariance structures across corpora . We showcase the performance of our system in three different cross-corpus experimental setups : single-corpus training , two-corpora training and training on augmented ( mixed up ) data . Findings show that the prediction behavior of trained models heavily depends on the covariance structure of the training corpus , and mixup is very effective in improving cross-corpus acoustic emotion recognition performance of context dependent LSTM models . A novel method called PSO optimized hidden-layer visible deep stacking network ( PHVDSN ) is proposed for feature extraction and recognition of motor imagery electroencephalogram ( EEG ) signals . A prior knowledge is introduced into the intermediate layer of deep stacking network ( DSN ) and the hidden nodes are expanded by the unsupervised training of restricted Boltzmann machine ( RBM ) for the parameter initialization . Then particle swarm optimization ( PSO ) is applied to optimize the input weights , aiming at alleviating the risk of being immersed in the curse of dimensionality . Third : The Reach Cage was used to study the encoding of walk-and-reach motor goals in the three cortical areas of the frontoparietal reach network ( posterior parietal reach region PRR , dorsal premotor cortex PMd and primary motor cortex M1 ) in one rhesus macaque . Fourth : Description of the eXperimental Behavioral Instrument ( XBI ) ; A cage-based training device allowing the training of rhesus macaques to typical behavioral tasks used in sensorimotor neuroscience without the need for the animals to leave their housing environment . Fifth : The XBI was used to develop a fully automatized training regime for which the training progress was controlled only by the task performance of the individual animal rather than a human trainer . For the development of a new-generation training system for spinal surgery , an iterative development cycle based on four steps was used . By using ( i ) empathy , ( ii ) cognitive model , ( iii ) prototyping and testing , and ( iv ) validation , an interdisciplinary development team was able to successfully build and validate a training system for lumbar disc surgery . The article describes key aspects of digital HR management , which is necessary to raise the efficiency of HR departments ’ functioning and to provide managers and specialists in the field of management with advanced tools for selection of skilled personnel meeting the company requirements . The authors formulated 16 methods , tools and technologies needed for organization of digital HR management , including the on-line structure of the company on digital platform , the system of on-line monitoring of goal attainment , electronic document turnover with digital signature , integration with external digital platforms , the system of on-line personnel training , individual employee adaptation , staff analysis on the basis of mathematic modeling and the use of artificial intellect . As experience shows , as a result of artificial intellect application in HR management the possibility of selecting adequate personnel becomes much higher . In this paper , we study the impact of imperfect channel state information at the transmitter and receiver ( CSITR ) on the diversity-multiplexing tradeoff in single-input multi-output (SIMO)/multi- input single-output ( MISO ) fading channels with two- way training . We consider two two-way training strategies : 1 ) the destination initiates the training , and 2 ) the source initiates the training . This paper proposes a revised counter-propagation network ( CPN ) model by integrating rough set in structural damage detection , applicable for processing redundant and uncertain information as well as assessing structural health states . Firstly , rough set is used in the model to deal with a large volume of data ; secondly , a revised training algorithm is developed to improve the capabilities of the CPN model ; and lastly , the least input vectors are input to the revised CPN ( RCPN ) model , hence the rough set-based RCPN is proposed in the paper . To validate the model proposed , numerical experiments are conducted , and , as a result , six damage patterns have been successfully identified in a steel frame . As SNIPER operates on resampled low resolution chips ( 512x512 pixels ) , it can have a batch size as large as on a single GPU even with a ResNet - 101 backbone . Therefore it can benefit from batch - normalization during training without the need for synchronizing batch - normalization statistics across GPUs . SNIPER brings training of instance level recognition tasks like object detection closer to the protocol for image classification and suggests that the commonly accepted guideline that it is important to train on high resolution images for instance level visual recognition tasks might not be correct . where is the manually labeled 2D landmark locations , is a constant - dim vector storing the indexes of 3D vertices corresponding to the labeled 2D landmarks . Unlike the three losses above , these landmark annotations are “ golden ” groundtruth , and hence can be used during the entire training process . Different from traditional face alignment work where the shape bases are fixed , our work jointly learns the bases functions ( i.e. , the shape decoder ) as well . subsection : Pre - processing In the training phase , given an input image , we use the HyperFace method for face detection and fiducial point extraction . The HyperFace detector automatically extracts many faces from a given image . 1 ( i.e. , setting Table 6 . Multi - task training ( forth column per group ) improves mAP over piecewise training ( third column per group ) . λ = 0 ) . We name our method Proposal Cluster Learning ( PCL ) because it learns refined instance classifiers based on proposal clusters . To implement our idea effectively and efficiently , we further propose an online training approach . Our network has multiple output streams as in Fig . In other words , during independent training and during joint training . The loss equations shown in this section are for independent training stage but can be easily modified to joint training by replacing with . Intuitively , the total variations of images could be decomposed into multiple levels , with higher - level semantic variations ( e.g. , attributes , object categories , rough shapes ) and lower - level variations ( e.g. , detailed contours and textures , background clutters ) . However , when the algorithm tries to update the annotation for one named variable , the nodes associated with that variable in all graphs are updated . During training , all labels for intermediate steps are available to us from the data generation process , so the training process again can be decomposed to single output single graph training . A more complex scenario allowing for nested data structures ( e.g. , list of lists ) was discussed in . The compatibility transform parameters of the CRF - RNN were initialized using the Potts model , and kernel width and weight parameters were obtained from a cross - validation process . We found that such initialization results in faster convergence of training . During the training phase , parameters of the whole network were optimized end - to - end using the back - propagation algorithm . Figure [ reference ] shows the effect of this form of sampling on convergence . We find that sampling does not have a significant effect on convergence rate compared to whole image training , but takes significantly more time due to the larger number of images that need to be considered per batch . We therefore choose unsampled , whole image training in our other experiments . The choice is motivated by the low time complexity of A + both at training and testing and the superior performance that it achieves . A + and our improved methods share the same features for LR and HR patches and the same dictionary training ( K - SVD [ reference ] ) as the ANR [ reference ] and Zeyde [ reference ] methods . The LR features are vertical and horizontal gradient responses , PCA projected for 99 % energy preservation . However , in many operatational conditions , this dataset will not be available . To study these effects , we constructed a dataset by sampling 70 K images from CASIA balanced over classes , and pre - encoding these images for template adaptation training . Figure [ reference ] ( bottom ) shows that this results in slightly reduced verification performance . But this solution ignores the derivative the proposal boxes ’ coordinates that are also network responses , so is approximate . In our experiments , we have empirically found this solver produces close results , yet reduces the training time by about 25 - 50 % comparing with alternating training . This solver is included in our released Python code . We further compare against the iterative classification algorithm ( ICA ) proposed in in conjunction with two logistic regression classifiers , one for local node features alone and one for relational classification using local features and an aggregation operator as described in . We first train the local classifier using all labeled training set nodes and use it to bootstrap class labels of unlabeled nodes for relational classifier training . We run iterative classification ( relational classifier ) with a random node ordering for 10 iterations on all unlabeled nodes ( bootstrapped using the local classifier ) . Thus , they explored different ways , e.g. intra - class similarity [ reference ] , inter - class variance [ reference ] , and distance mapping relation [ reference ] , to initialize the training object hypotheses . Later on , some recent WOD methods started to pay more attention to the optimization procedure designed for better training object detectors under the weak supervision . For example , [ reference ][ reference ] proposed to smooth the object formulation to better obtain the optimal solutions . We update both embeddings and parameters in transductive learning , and update only parameters in inductive learning . Before the joint training procedure , we apply a number of training iterations that optimize the unsupervised loss alone and use the learned embeddings as initialization for joint training . [ tb ] Model Training ( Transductive ) Input : , , , , batch iterations and sizes to Sample a batch of labeled instances of size This paper revisits several objective functions that have been commonly used for structured prediction tasks in NLP gimpel + smith2010acl and apply them to a neural sequence to sequence model gehring2017icml ( § [ reference ] ) . Specifically , we consider likelihood training at the sequence - level , a margin loss as well as expected risk training . We also investigate several combinations of global losses with token - level likelihood . Indeed , work by cross2016incremental , published while working on the camera - ready version of this paper , show that the same methodology as ours is highly effective also for greedy , transition - based constituency parsing , surpassing the beam - based architecture of Vinyals et al . ( 88.3F vs. 89.8F points ) when trained on the Penn Treebank dataset and without using orthogonal methods such as ensembling and up - training . subsection : Bidirectional Recurrent Neural Networks For the sake of direct comparability we left the learning rates unchanged , but there might be better hyper - parameters for our variants of InPlace - ABN and InPlace - ABNsync . In essence , our results closely approach reported numbers for Cityscapes in , which however include more training data in the body model training or are based on already trained models like DeepLabV2 . For COCO - Stuff , results are not directly comparable to the original publication since they used a lower - performing VGG - 16 network for the body . The extra layers are initialized randomly as in . We adopt an image - centric training framework : the shared convolutional layers are computed on the entire image , while the RoIs are randomly sampled for computing loss functions . In our system , each mini - batch involves 1 image , 256 sampled anchors for stage 1 as in , and 64 sampled RoIs for stages 2 and 3 . With the above networks pre - trained on ImageNet , we extract these representations from the images in the target datasets and re - train SVM classifiers . In the SVM training , we intentionally do not use any data augmentation ( flip / multi - view ) . We l - normalize the features for SVM training . In this paper , failure modes are identified by hidden Markov models ( HMMs ) and fault severities are estimated by filtering methods , leading to a decrease in the number of HMM states and low computational requirements . To adapt to changing environments , a new online learning algorithm is developed . In this algorithm , HMM parameters are obtained based on their posterior distributions given new observations , thereby avoiding the need for accurate recurrence equations . Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization ( NMF ) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes , respectively . We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions . This ensures robust shape inference and a shape model constructed from contextually relevant shapes . The abundance of facts characteristics brought up in history teaching , so it often put history learning as facts mastery learning through rote learning activity . This writing attempt to lift other approach in history learning namely online based learning that was identified can improve the ability to think . The results show QuestGarden ( LCMS ) or Edmodo ( LMS ) can be implemented in the history teaching learning , though both have different characters and use . In this study adopted Marczewski 's Gamification Framework concept which consists of 8 questions : 1 ) what is being gamified ; 2 ) why is it being gamified ; 3 ) who are the users ; 4 ) how is it being gamified ; 5 ) analytics are set up ; 6 ) tested with users ; 7 ) acted on feedback ; and 8) released the solution . Based on the results of data analysis and discussion , it can be concluded that 1 ) the learning process in the course of Renewal of Economic Education Learning can be done with the concept of gamification by combining face-to-face learning in the classroom with online learning learning ; 2 ) student response in learning process with gamification concept mostly interested because it is new for them ; 3 ) there is a nurturant effect for as long as the concept of gamification is applied in the class It is expected that e-learning system can increase the frequency of learningprocess , enrich learning materials , and increase student participation in learning . Theobjective of this research is to find out the implementation of MOODLE - basedonline learning system at the Faculty of Curriculum and Educational Technology(Kurikulum dan Teknologi Pendidikan ) at Semarang State University . The type ofresearch applied is Research and Development ( Penelitian dan pengembangan ) . An alternative model of learning which enables support the learning process occurs wherever and whenever is a model of learning Online Learning . Methods used in this research is in research development beginning from activity which is , analysis introduction , design , evaluation and revision ( akker , 1999 : 7 ) . Chawdhry , Paullet and Benjamin state that an increasing number of students are enrolling in online learning to complete their degrees , compete in today ’s job market , and advance in their careers ( 2011 ) . Advantages Of Online Learning Online learning offers advantages to both schools as well as students . For schools online courses offer higher education institutions innovative ways to target adult learners wanting to continue their education but are constrained ( Coppola , Hiltz , & Roxanne , 2002 ) . At present , the application research of “ Internet + education ” in college English teaching focuses on the following aspects : 1.1 Researches of College English Classroom Based on blended Learning The concept of blended learning was proposed at the beginning of the century in the context of the information age . blended learning can combine a variety of teaching methods and teaching techniques , combining the advantages of classroom teaching and online online learning . Fully mobilize the autonomy and flexibility of students ' learning . Learning methods are shifting from instructor-oriented to learner-oriented-Learning , referring to learning via the Internet , provides learner with an efficient , flexible and personalized way to learn . It offers learning-on-fly and reduces overall learning cost . This paper describes the demands for e-Learning and related research , and presents a variety of data mining technologies that can integrate with elearning system to explore the problems faced by Higher Education system and discovering underlying knowledge from Educational Data base . This paper presents a method to improve the traditional mixture of deformable part models ( MDPM ) method from the learning perspective . First , an object part configuration learning algorithm based on group sparsity constraint is introduced to automatically discover the object part number , size , and location . The algorithm imposes two additional regularization terms in addition to the standard hinge loss function . This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression ( LWPR ) and on a bio-inspired module , such as a cerebellar-like engine . This hybrid architecture takes full advantage of the machine learning module ( LWPR kernel ) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task . Furthermore , we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model . Moreover , the creation and sharing of resources and information as afforded by Web 2.0 technologies can also improve a student 's learning experience . Web 2.0 technologies such as wikis can be used to enable and support new and varied forms of group work learning . Wikis provide tools for measuring students ' contributions , such as the number , size and regularity of their contributions . One of the main findings was the unbalanced development between two sub-systems , which means that the educational institution focuses much on the development of course packages while to some extent neglects the importance of providing sufficient learning support and services . It is one of the most urgent tasks to establish a more powerful , flexible , and effective learning support and services system in order to assure quality distance learning . These models are typically tuned manually , a brittle and laborious process . Autonomous model learning is a promising alternative to manual calibration , but previous work has assumed the presence of an accurate action or sensor model in order to train the other model . This paper presents an adaptation of the Expectation-Maximization ( EM ) algorithm to enable a mobile robot to learn both its action and sensor model functions , starting without an accurate version of either . We consider the use of deep neural network ( DNN ) to develop a decision-directed (DD)-channel estimation ( CE ) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments . We propose the use of DNN for $ k$ -step channel prediction for space-time block code ( STBC ) , and show that deep learning (DL)-based DD-CE can remove the need for Doppler rate estimation in fast time-varying quasi stationary channels , where the Doppler rate varies from one packet to another . Doppler rate estimation in this kind of vehicular channels is remarkably challenging and requires a large number of pilots and preambles , leading to lower power and spectral efficiency . The correctness of labels has a significant influence on disease discrimination . However , the labelling of disease images by professional doctors is a time-consuming and labour-intensive project ; therefore , obtaining correct and high-quality labels is a difficult task in medical image learning . This paper proposes a deep convolutional neural network-based label completion and correction strategy for supervised medical image learning . It is worth noting that , apart from MAP inference , Vemulapalli et al . derived an end - to - end trainable patch - based denoising network based on Gaussian Conditional Random Field ( GCRF ) inference . MAP inference guided discriminative learning usually requires much fewer inference steps , and is very efficient in image denoising . It also has clear interpretability because the discriminative architecture is derived from optimization algorithms such as HQS and gradient descent . [ ] proposes one of the earliest methods that enables multi - scale deep features for salient object detection . Later , multi - context deep learning and multi - level convolutional features are proposed for improving salient object detection . More recently , Hou et al . context2vec Melamud2016context2vecLG uses a bidirectional Long Short Term Memory [ LSTM ; ] [ ] LSTM : Hochreiter1997 to encode the context around a pivot word . Other approaches for learning contextual embeddings include the pivot word itself in the representation and are computed with the encoder of either a supervised neural machine translation ( MT ) system [ CoVe ; ] [ ] McCann2017LearnedIT or an unsupervised language model Peters2017SemisupervisedST . The current state of the art , set forth by , extracts contextual information from the surrounding utterances using long short - term memory ( LSTM ) . fuses different modalities with deep learning - based tools . uses tensor fusion . We hope that the present paper can contribute to a larger effort towards ever more private and ubiquitous artificial intelligence . Future research directions will include private analytics , allowing to receive privacy - preserving feedback from assistant usage , and federated learning , as a complement to data generation . section : Acknowledgments Problem . The key problem with recent methods for learning flow is that they typically take two frames , stack them together , and apply a convolutional network architecture . When the motions between frames are larger than one ( or a few ) pixels , spatio - temporal convolutional filters will not obtain meaningful responses . subsection : Learning representations of 3D orientations We describe the difficulties of training with fixed SO ( 3 ) parameterizations which will motivate the learning of object - specific representations . subsubsection : Regression . As an example , consider Car - Chicken object interactions in Freeway : learning that there is lower value associated with one Chicken instance hitting a Car instance can be generalized to all instances of those two classes . DISCO first performs a series of preprocessing steps to discover classes , during which no value function learning is performed . When the agent subsequently learns to play the game , DISCO generates features by detecting objects on the screen and classifying them . However , the length of dependencies captured by a dilated CNN is limited by its kernel size , whereas an RNN ’s autoregressive modeling can , in theory , capture potentially infinitely long dependencies with a small number of parameters . Recently , Yu et al . proposed learning - based RNNs with the ability to jump ( skim input text ) after seeing a few timestamps worth of data ; although the authors showed that the modified LSTM with jumping provides up to a six - fold speed increase , the efficiency gain is mainly in the testing phase . In this paper , we introduce the DilatedRNN , a neural connection architecture analogous to the dilated CNN , but under a recurrent setting .
CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation . In elaborately designed experiments , CL-CIS is evaluated together with general causality analysis system ( GCAS ) and general causality analysis system with learning ( GCAS-L ) , and it testified to its own capability and performance in construction of cause-effect relations . This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning .
This paper discusses a framework for describing and analysing how young students ( 5–6 years ) learn in technology with a view towards enhancing teaching and learning practice in technology . Examples of student work which demonstrate the complexity of learning in technology , and what young children can achieve with appropriate teaching strategies are presented . Holistic aspects as well as associated variables are highlighted .
This gate , working with max - pooling and positive activation function ( e.g. , Sigmoid ) , keeps away the artifacts from padding in all layers . Actually it creates a natural hierarchy of all - zero padding ( as illustrated in Figure [ reference ] ) , consisting of nodes in the neural net that would not contribute in the forward process ( as in prediction ) and backward propagation ( as in learning ) . subsection : Some Analysis on the Convolutional Architecture
the form of computation required to complete the task . In this work , we propose a meta - learning algorithm that is general and model - agnostic , in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure . Our focus is on deep neural network models , but we illustrate how our approach can easily handle different architectures and different problem settings , including classification , regression , and policy gradient reinforcement learning , with minimal modification .
However , we have observed that we obtain substantially better results by “ inpainting ” the values of the supervision signal on positions that were not originally annotated . For this we adopt a learning - based approach where we firstly train a “ teacher ” network ( depicted in Fig . [ reference ] ) to reconstruct the ground - truth values wherever these are observed , and then deploy it on the full image domain , yielding a dense supervision signal .
By virtue of its generality , the SPL theory has been widely applied to various tasks , such as multi - view clustering [ reference ] , multi - label propagation [ reference ] , multimedia event detection , and co - saliency detection . More recently , introduced the pre - defined learning curriculum to the conventional self - paced learning regime which can take into account both the helpful prior knowledge known before training and the self - learning progress during training . Inspired by this work , we design a task - specific learning curriculum and construct a unified SPCL model specifically for both the saliency detection and WOD tasks , through which both can be naturally related .
In advanced automatic systems , control algorithms are based on fuzzy logic methods and using of artificial neural networks . The application of the developed control actions shaper does not require complicated adjustment of the regulator parameters by means of expert assessments or the learning process . Analysis of scientific literature and publications revealed the absence of standard program libraries for programming such shapers on microcontrollers or programmable logic controllers .
The study was conducted in two cycles , and each cycle consists of two meeting . The data of this study is the action of the teacher in teaching , learning materials , students activity in learning learning process , and the result of student learning in writing the story . Data collection techniques used in this study were observation , interviews , field notes , questionnaires , and test performance .
One of the most LMS in Indonesia is moodle , which is an open source LMS platform that can be developed independently . This study uses features to find features that can be used in the learning and learning process on SNS SN . The results of the features that will be implemented into the moodle system use system development methods , namely component-based software engineering ( CBSE ) , CBSE has several stages of development namely domain engineering , component engineering , system and software design , configuration and configuration , and testing and validation .
We propose a learning process based approach to address the well-known implicit profiling method in group learning recommender system . Our approach makes use of likelihood control to integrate the learning step coefficient of each learner into ratings .
From fluffy clouds to a meaningful learning process Learning is increasingly distributed over virtual , simulation and real-life environments . Collaboration takes place via cloud-services and social media chosen by the teacher , or by the students themselves .
The technique chosen for the learning process was the Map of Kohonen , because it is a classic technique with a self-learning capacity . The solution investigated was part of a huge problem : the learning process of tasks by articulated robots , for example the pick-and-place task . The results that were found indicated that the proposed solution was able to learn in two scenarios : data of movements of the end effector in line and data of end effector realizing movements in a plane .
subsection : Learning The learning goal is to maximize the estimated probability of the actual summary . We adopt the common negative log - likelihood ( NLL ) as the loss function .
[ reference ] ) . From a learning perspective , the representation of the source sequence in the ByteNet is resolution preserving ; the representation sidesteps the need for memorization and allows for maximal bandwidth between encoder and decoder . In addition , the distance traversed by forward and backward signals between any input and output tokens corresponds to the fixed depth of the networks and is largely independent of the distance between the tokens .
ASAC gradually reduces a parameter called an entropy temperature , which regulates the entropy of the control policy . In this way , the proposed method can narrow down the search space during training , and reduce the duration of demanding data collection processes in real-world experiments . For the verification of the robustness and the controllability of our robot and the RL algorithm , experiments for zig-zagging path tracking and obstacle avoidance were conducted , and the robot successfully finished the missions with only an hour of training time .
The proposed scheme is built based on feed forward neural network model with multi-hidden layers . The model consists of four phases : data acquisition , pre-processing , feature extraction and neural network training and testing . In this study the neural network model provides higher forecast accuracy than other proposed methods .
For further comparison , this work also compares Support Vector Machines to K-nearest neighbours , known for its simplicity and solid results in other domains . The third part of this dissertation investigates the automatic generation of training data . Classifier accuracy depends on good quality training data -- the wider the training data spectrum , the higher the classifier 's accuracy .
For training National Guard units , the U.S. Army National Guard will field 21 combat vehicle training simulators called mobile trainers . Each National Guard unit must train at a station that is not farther than a specified maximum travel distance from its armory .
We propose to employ a simple but effective strategy called “ mixup ” to overcome the gap in feature-target and target-target covariance structures across corpora . We showcase the performance of our system in three different cross-corpus experimental setups : single-corpus training , two-corpora training and training on augmented ( mixed up ) data . Findings show that the prediction behavior of trained models heavily depends on the covariance structure of the training corpus , and mixup is very effective in improving cross-corpus acoustic emotion recognition performance of context dependent LSTM models .
A novel method called PSO optimized hidden-layer visible deep stacking network ( PHVDSN ) is proposed for feature extraction and recognition of motor imagery electroencephalogram ( EEG ) signals . A prior knowledge is introduced into the intermediate layer of deep stacking network ( DSN ) and the hidden nodes are expanded by the unsupervised training of restricted Boltzmann machine ( RBM ) for the parameter initialization . Then particle swarm optimization ( PSO ) is applied to optimize the input weights , aiming at alleviating the risk of being immersed in the curse of dimensionality .
Third : The Reach Cage was used to study the encoding of walk-and-reach motor goals in the three cortical areas of the frontoparietal reach network ( posterior parietal reach region PRR , dorsal premotor cortex PMd and primary motor cortex M1 ) in one rhesus macaque . Fourth : Description of the eXperimental Behavioral Instrument ( XBI ) ; A cage-based training device allowing the training of rhesus macaques to typical behavioral tasks used in sensorimotor neuroscience without the need for the animals to leave their housing environment . Fifth : The XBI was used to develop a fully automatized training regime for which the training progress was controlled only by the task performance of the individual animal rather than a human trainer .
For the development of a new-generation training system for spinal surgery , an iterative development cycle based on four steps was used . By using ( i ) empathy , ( ii ) cognitive model , ( iii ) prototyping and testing , and ( iv ) validation , an interdisciplinary development team was able to successfully build and validate a training system for lumbar disc surgery .
The article describes key aspects of digital HR management , which is necessary to raise the efficiency of HR departments ’ functioning and to provide managers and specialists in the field of management with advanced tools for selection of skilled personnel meeting the company requirements . The authors formulated 16 methods , tools and technologies needed for organization of digital HR management , including the on-line structure of the company on digital platform , the system of on-line monitoring of goal attainment , electronic document turnover with digital signature , integration with external digital platforms , the system of on-line personnel training , individual employee adaptation , staff analysis on the basis of mathematic modeling and the use of artificial intellect . As experience shows , as a result of artificial intellect application in HR management the possibility of selecting adequate personnel becomes much higher .
In this paper , we study the impact of imperfect channel state information at the transmitter and receiver ( CSITR ) on the diversity-multiplexing tradeoff in single-input multi-output (SIMO)/multi- input single-output ( MISO ) fading channels with two- way training . We consider two two-way training strategies : 1 ) the destination initiates the training , and 2 ) the source initiates the training .
This paper proposes a revised counter-propagation network ( CPN ) model by integrating rough set in structural damage detection , applicable for processing redundant and uncertain information as well as assessing structural health states . Firstly , rough set is used in the model to deal with a large volume of data ; secondly , a revised training algorithm is developed to improve the capabilities of the CPN model ; and lastly , the least input vectors are input to the revised CPN ( RCPN ) model , hence the rough set-based RCPN is proposed in the paper . To validate the model proposed , numerical experiments are conducted , and , as a result , six damage patterns have been successfully identified in a steel frame .
As SNIPER operates on resampled low resolution chips ( 512x512 pixels ) , it can have a batch size as large as on a single GPU even with a ResNet - 101 backbone . Therefore it can benefit from batch - normalization during training without the need for synchronizing batch - normalization statistics across GPUs . SNIPER brings training of instance level recognition tasks like object detection closer to the protocol for image classification and suggests that the commonly accepted guideline that it is important to train on high resolution images for instance level visual recognition tasks might not be correct .
where is the manually labeled 2D landmark locations , is a constant - dim vector storing the indexes of 3D vertices corresponding to the labeled 2D landmarks . Unlike the three losses above , these landmark annotations are “ golden ” groundtruth , and hence can be used during the entire training process . Different from traditional face alignment work where the shape bases are fixed , our work jointly learns the bases functions ( i.e. , the shape decoder ) as well .
subsection : Pre - processing In the training phase , given an input image , we use the HyperFace method for face detection and fiducial point extraction . The HyperFace detector automatically extracts many faces from a given image .
1 ( i.e. , setting Table 6 . Multi - task training ( forth column per group ) improves mAP over piecewise training ( third column per group ) . λ = 0 ) .
We name our method Proposal Cluster Learning ( PCL ) because it learns refined instance classifiers based on proposal clusters . To implement our idea effectively and efficiently , we further propose an online training approach . Our network has multiple output streams as in Fig .
In other words , during independent training and during joint training . The loss equations shown in this section are for independent training stage but can be easily modified to joint training by replacing with . Intuitively , the total variations of images could be decomposed into multiple levels , with higher - level semantic variations ( e.g. , attributes , object categories , rough shapes ) and lower - level variations ( e.g. , detailed contours and textures , background clutters ) .
However , when the algorithm tries to update the annotation for one named variable , the nodes associated with that variable in all graphs are updated . During training , all labels for intermediate steps are available to us from the data generation process , so the training process again can be decomposed to single output single graph training . A more complex scenario allowing for nested data structures ( e.g. , list of lists ) was discussed in .
The compatibility transform parameters of the CRF - RNN were initialized using the Potts model , and kernel width and weight parameters were obtained from a cross - validation process . We found that such initialization results in faster convergence of training . During the training phase , parameters of the whole network were optimized end - to - end using the back - propagation algorithm .
Figure [ reference ] shows the effect of this form of sampling on convergence . We find that sampling does not have a significant effect on convergence rate compared to whole image training , but takes significantly more time due to the larger number of images that need to be considered per batch . We therefore choose unsampled , whole image training in our other experiments .
The choice is motivated by the low time complexity of A + both at training and testing and the superior performance that it achieves . A + and our improved methods share the same features for LR and HR patches and the same dictionary training ( K - SVD [ reference ] ) as the ANR [ reference ] and Zeyde [ reference ] methods . The LR features are vertical and horizontal gradient responses , PCA projected for 99 % energy preservation .
However , in many operatational conditions , this dataset will not be available . To study these effects , we constructed a dataset by sampling 70 K images from CASIA balanced over classes , and pre - encoding these images for template adaptation training . Figure [ reference ] ( bottom ) shows that this results in slightly reduced verification performance .
But this solution ignores the derivative the proposal boxes ’ coordinates that are also network responses , so is approximate . In our experiments , we have empirically found this solver produces close results , yet reduces the training time by about 25 - 50 % comparing with alternating training . This solver is included in our released Python code .
We further compare against the iterative classification algorithm ( ICA ) proposed in in conjunction with two logistic regression classifiers , one for local node features alone and one for relational classification using local features and an aggregation operator as described in . We first train the local classifier using all labeled training set nodes and use it to bootstrap class labels of unlabeled nodes for relational classifier training . We run iterative classification ( relational classifier ) with a random node ordering for 10 iterations on all unlabeled nodes ( bootstrapped using the local classifier ) .
Thus , they explored different ways , e.g. intra - class similarity [ reference ] , inter - class variance [ reference ] , and distance mapping relation [ reference ] , to initialize the training object hypotheses . Later on , some recent WOD methods started to pay more attention to the optimization procedure designed for better training object detectors under the weak supervision . For example , [ reference ][ reference ] proposed to smooth the object formulation to better obtain the optimal solutions .
We update both embeddings and parameters in transductive learning , and update only parameters in inductive learning . Before the joint training procedure , we apply a number of training iterations that optimize the unsupervised loss alone and use the learned embeddings as initialization for joint training . [ tb ] Model Training ( Transductive ) Input : , , , , batch iterations and sizes to Sample a batch of labeled instances of size
This paper revisits several objective functions that have been commonly used for structured prediction tasks in NLP gimpel + smith2010acl and apply them to a neural sequence to sequence model gehring2017icml ( § [ reference ] ) . Specifically , we consider likelihood training at the sequence - level , a margin loss as well as expected risk training . We also investigate several combinations of global losses with token - level likelihood .
Indeed , work by cross2016incremental , published while working on the camera - ready version of this paper , show that the same methodology as ours is highly effective also for greedy , transition - based constituency parsing , surpassing the beam - based architecture of Vinyals et al . ( 88.3F vs. 89.8F points ) when trained on the Penn Treebank dataset and without using orthogonal methods such as ensembling and up - training . subsection : Bidirectional Recurrent Neural Networks
For the sake of direct comparability we left the learning rates unchanged , but there might be better hyper - parameters for our variants of InPlace - ABN and InPlace - ABNsync . In essence , our results closely approach reported numbers for Cityscapes in , which however include more training data in the body model training or are based on already trained models like DeepLabV2 . For COCO - Stuff , results are not directly comparable to the original publication since they used a lower - performing VGG - 16 network for the body .
The extra layers are initialized randomly as in . We adopt an image - centric training framework : the shared convolutional layers are computed on the entire image , while the RoIs are randomly sampled for computing loss functions . In our system , each mini - batch involves 1 image , 256 sampled anchors for stage 1 as in , and 64 sampled RoIs for stages 2 and 3 .
With the above networks pre - trained on ImageNet , we extract these representations from the images in the target datasets and re - train SVM classifiers . In the SVM training , we intentionally do not use any data augmentation ( flip / multi - view ) . We l - normalize the features for SVM training .
In this paper , failure modes are identified by hidden Markov models ( HMMs ) and fault severities are estimated by filtering methods , leading to a decrease in the number of HMM states and low computational requirements . To adapt to changing environments , a new online learning algorithm is developed . In this algorithm , HMM parameters are obtained based on their posterior distributions given new observations , thereby avoiding the need for accurate recurrence equations .
Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization ( NMF ) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes , respectively . We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions . This ensures robust shape inference and a shape model constructed from contextually relevant shapes .
The abundance of facts characteristics brought up in history teaching , so it often put history learning as facts mastery learning through rote learning activity . This writing attempt to lift other approach in history learning namely online based learning that was identified can improve the ability to think . The results show QuestGarden ( LCMS ) or Edmodo ( LMS ) can be implemented in the history teaching learning , though both have different characters and use .
In this study adopted Marczewski 's Gamification Framework concept which consists of 8 questions : 1 ) what is being gamified ; 2 ) why is it being gamified ; 3 ) who are the users ; 4 ) how is it being gamified ; 5 ) analytics are set up ; 6 ) tested with users ; 7 ) acted on feedback ; and 8) released the solution . Based on the results of data analysis and discussion , it can be concluded that 1 ) the learning process in the course of Renewal of Economic Education Learning can be done with the concept of gamification by combining face-to-face learning in the classroom with online learning learning ; 2 ) student response in learning process with gamification concept mostly interested because it is new for them ; 3 ) there is a nurturant effect for as long as the concept of gamification is applied in the class
It is expected that e-learning system can increase the frequency of learningprocess , enrich learning materials , and increase student participation in learning . Theobjective of this research is to find out the implementation of MOODLE - basedonline learning system at the Faculty of Curriculum and Educational Technology(Kurikulum dan Teknologi Pendidikan ) at Semarang State University . The type ofresearch applied is Research and Development ( Penelitian dan pengembangan ) .
An alternative model of learning which enables support the learning process occurs wherever and whenever is a model of learning Online Learning . Methods used in this research is in research development beginning from activity which is , analysis introduction , design , evaluation and revision ( akker , 1999 : 7 ) .
Chawdhry , Paullet and Benjamin state that an increasing number of students are enrolling in online learning to complete their degrees , compete in today ’s job market , and advance in their careers ( 2011 ) . Advantages Of Online Learning Online learning offers advantages to both schools as well as students . For schools online courses offer higher education institutions innovative ways to target adult learners wanting to continue their education but are constrained ( Coppola , Hiltz , & Roxanne , 2002 ) .
At present , the application research of “ Internet + education ” in college English teaching focuses on the following aspects : 1.1 Researches of College English Classroom Based on blended Learning The concept of blended learning was proposed at the beginning of the century in the context of the information age . blended learning can combine a variety of teaching methods and teaching techniques , combining the advantages of classroom teaching and online online learning . Fully mobilize the autonomy and flexibility of students ' learning .
Learning methods are shifting from instructor-oriented to learner-oriented-Learning , referring to learning via the Internet , provides learner with an efficient , flexible and personalized way to learn . It offers learning-on-fly and reduces overall learning cost . This paper describes the demands for e-Learning and related research , and presents a variety of data mining technologies that can integrate with elearning system to explore the problems faced by Higher Education system and discovering underlying knowledge from Educational Data base .
This paper presents a method to improve the traditional mixture of deformable part models ( MDPM ) method from the learning perspective . First , an object part configuration learning algorithm based on group sparsity constraint is introduced to automatically discover the object part number , size , and location . The algorithm imposes two additional regularization terms in addition to the standard hinge loss function .
This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression ( LWPR ) and on a bio-inspired module , such as a cerebellar-like engine . This hybrid architecture takes full advantage of the machine learning module ( LWPR kernel ) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task . Furthermore , we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model .
Moreover , the creation and sharing of resources and information as afforded by Web 2.0 technologies can also improve a student 's learning experience . Web 2.0 technologies such as wikis can be used to enable and support new and varied forms of group work learning . Wikis provide tools for measuring students ' contributions , such as the number , size and regularity of their contributions .
One of the main findings was the unbalanced development between two sub-systems , which means that the educational institution focuses much on the development of course packages while to some extent neglects the importance of providing sufficient learning support and services . It is one of the most urgent tasks to establish a more powerful , flexible , and effective learning support and services system in order to assure quality distance learning .
These models are typically tuned manually , a brittle and laborious process . Autonomous model learning is a promising alternative to manual calibration , but previous work has assumed the presence of an accurate action or sensor model in order to train the other model . This paper presents an adaptation of the Expectation-Maximization ( EM ) algorithm to enable a mobile robot to learn both its action and sensor model functions , starting without an accurate version of either .
We consider the use of deep neural network ( DNN ) to develop a decision-directed (DD)-channel estimation ( CE ) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments . We propose the use of DNN for $ k$ -step channel prediction for space-time block code ( STBC ) , and show that deep learning (DL)-based DD-CE can remove the need for Doppler rate estimation in fast time-varying quasi stationary channels , where the Doppler rate varies from one packet to another . Doppler rate estimation in this kind of vehicular channels is remarkably challenging and requires a large number of pilots and preambles , leading to lower power and spectral efficiency .
The correctness of labels has a significant influence on disease discrimination . However , the labelling of disease images by professional doctors is a time-consuming and labour-intensive project ; therefore , obtaining correct and high-quality labels is a difficult task in medical image learning . This paper proposes a deep convolutional neural network-based label completion and correction strategy for supervised medical image learning .
It is worth noting that , apart from MAP inference , Vemulapalli et al . derived an end - to - end trainable patch - based denoising network based on Gaussian Conditional Random Field ( GCRF ) inference . MAP inference guided discriminative learning usually requires much fewer inference steps , and is very efficient in image denoising . It also has clear interpretability because the discriminative architecture is derived from optimization algorithms such as HQS and gradient descent .
[ ] proposes one of the earliest methods that enables multi - scale deep features for salient object detection . Later , multi - context deep learning and multi - level convolutional features are proposed for improving salient object detection . More recently , Hou et al .
context2vec Melamud2016context2vecLG uses a bidirectional Long Short Term Memory [ LSTM ; ] [ ] LSTM : Hochreiter1997 to encode the context around a pivot word . Other approaches for learning contextual embeddings include the pivot word itself in the representation and are computed with the encoder of either a supervised neural machine translation ( MT ) system [ CoVe ; ] [ ] McCann2017LearnedIT or an unsupervised language model Peters2017SemisupervisedST .
The current state of the art , set forth by , extracts contextual information from the surrounding utterances using long short - term memory ( LSTM ) . fuses different modalities with deep learning - based tools . uses tensor fusion .
We hope that the present paper can contribute to a larger effort towards ever more private and ubiquitous artificial intelligence . Future research directions will include private analytics , allowing to receive privacy - preserving feedback from assistant usage , and federated learning , as a complement to data generation . section : Acknowledgments
Problem . The key problem with recent methods for learning flow is that they typically take two frames , stack them together , and apply a convolutional network architecture . When the motions between frames are larger than one ( or a few ) pixels , spatio - temporal convolutional filters will not obtain meaningful responses .
subsection : Learning representations of 3D orientations We describe the difficulties of training with fixed SO ( 3 ) parameterizations which will motivate the learning of object - specific representations . subsubsection : Regression .
As an example , consider Car - Chicken object interactions in Freeway : learning that there is lower value associated with one Chicken instance hitting a Car instance can be generalized to all instances of those two classes . DISCO first performs a series of preprocessing steps to discover classes , during which no value function learning is performed . When the agent subsequently learns to play the game , DISCO generates features by detecting objects on the screen and classifying them .
However , the length of dependencies captured by a dilated CNN is limited by its kernel size , whereas an RNN ’s autoregressive modeling can , in theory , capture potentially infinitely long dependencies with a small number of parameters . Recently , Yu et al . proposed learning - based RNNs with the ability to jump ( skim input text ) after seeing a few timestamps worth of data ; although the authors showed that the modified LSTM with jumping provides up to a six - fold speed increase , the efficiency gain is mainly in the testing phase . In this paper , we introduce the DilatedRNN , a neural connection architecture analogous to the dilated CNN , but under a recurrent setting .
Improving Causality Induction with Category Learning Application of an analytical framework to describe young students' learning in technology Convolutional Neural Network Architectures for Matching Natural Language Sentences Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning Using of Control Actions Shaper for Movement Control Process of Mobile Platform PENINGKATAN KETERAMPILAN MENULIS CERITA MELALUI TEKNIK TIRU MODEL SISWA KELAS III SD NEGERI 200208 PADANGSIDIMPUAN IMPLEMENTASI METODE COMPONENT-BASED SOFTWARE ENGINEERING (CBSE) DALAM MENGEMBANGKAN LEARNING MANAGEMENT SYSTEM (LMS) Building Learner Profile for Group Learning Recommender System from Learning Process Pilvenhattaroista mielekkääseen oppimisprosessiin Self-learning in the inverse kinematics of robotic arm Faithful to the Original: Fact Aware Neural Abstractive Summarization Neural Machine Translation in Linear Time Learning to Walk a Tripod Mobile Robot Using Nonlinear Soft Vibration Actuators With Entropy Adaptive Reinforcement Learning Earthquakes magnitude predication using artificial neural network in northern Red Sea area Detecting worm mutations using machine learning The U.S. Army National Guard's Mobile Training Simulators Location and Routing Problem Context Modeling for Cross-Corpus Dimensional Acoustic Emotion Recognition: Challenges and Mixup Hidden-layer visible deep stacking network optimized by PSO for motor imagery EEG recognition Decoding Cortical Motor Goal Representations in a 3D Real-World Environment Design and development process of a next-generation training system for spinal surgery ОРГАНИЗАЦИЯ ЦИФРОВОГО УПРАВЛЕНИЯ ПЕРСОНАЛОМ Diversity and Multiplexing Tradeoff in SIMO/MISO Channels: Two-Way Training and Power Control A Revised Counter-Propagation Network Model Integrating Rough Set for Structural Damage Detection SNIPER: Efficient Multi-Scale Training Nonlinear 3D Face Morphable Model Triplet probabilistic embedding for face verification and clustering Fast R-CNN PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Stacked Generative Adversarial Networks Gated Graph Sequence Neural Networks Conditional Random Fields as Recurrent Neural Networks Fully Convolutional Networks for Semantic Segmentation Seven Ways to Improve Example-Based Single Image Super Resolution Template adaptation for face verification and identification Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Semi-Supervised Classification with Graph Convolutional Networks Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning Revisiting Semi-Supervised Learning with Graph Embeddings Classical Structured Prediction Losses for Sequence to Sequence Learning Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations In-place Activated BatchNorm for Memory-Optimized Training of DNNs Instance-Aware Semantic Segmentation via Multi-task Network Cascades Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Fault Diagnosis of Components and Sensors in HVAC Air Handling Systems With New Types of Faults Online Relational Manifold Learning for Multiview Segmentation in Echocardiography CONSTRUCTING ONLINE BASED HISTORY LEARNING: COMPARISON OF LEARNING CONTENT MANAGEMENT SYSTEM (LCMS) TO LEARNING MANAGEMENT SYSTEM (LMS) EFEKTIFITAS GAMIFICATION BERBASIS BLENDED LEARNING PADA MATA KULIAH PENDIDIKAN EKONOMI PENGEMBANGAN SISTEM PEMBELAJARAN ONLINE BERBASIS MOODLE DI JURUSAN KURIKULUM DAN TEKNOLOGI PENDIDIKAN UNIVERSITAS NEGERI SEMARANG PENGEMBANGAN MODEL PEMBELAJARAN ONLINE LEARNING MATERI ELEKTROKIMIA PADA MATA KULIAH KIMIA TEKNIK DI FAKULTAS TEKNIK UNIVERSITAS NEGERI PADANG Student Access to Online Interaction Technologies: The Impact on Grade Delta Variance and Student Satisfaction Practical Research on College English Spoken Blended Learning Model under the Background of “Internet + education” Adaptive E-Learning System-A State of Art Balanced Mixture of Deformable Part Models With Automatic Part Configurations Bio-inspired adaptive feedback error learning architecture for motor control Developing a framework to assess students' contributions during wiki construction An Analysis of Learners' Interactions with Course Package and Learning Support Services in Distance Learning Maximum likelihood estimation of sensor and action model functions on a mobile robot Decision Directed Channel Estimation Based on Deep Neural Network $k$ -Step Predictor for MIMO Communications in 5G A Deep Convolutional Neural Network-Based Label Completion and Correction Strategy for Supervised Medical Image Learning FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising Res2Net: A New Multi-scale Backbone Architecture Deep contextualized word representations Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Optical Flow Estimation Using a Spatial Pyramid Network Implicit 3D Orientation Learning for 6D Object Detection from RGB Images The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) Dilated Recurrent Neural Networks
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NIPS ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IJCAI LARS/SBR AAAI ArXiv SPECOM ICC NeurIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) 2015 IEEE International Conference on Computer Vision (ICCV) IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR 2015 IEEE International Conference on Computer Vision (ICCV) IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Image Vis. Comput. IEEE Transactions on Pattern Analysis and Machine Intelligence ICLR IJCAI ICML NAACL-HLT Transactions of the Association for Computational Linguistics 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence DICTA ACE ICRA SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI IEEE Transactions on Image Processing IEEE transactions on pattern analysis and machine intelligence NAACL-HLT Knowl. Based Syst. ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV IJCAI NIPS
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