To evaluate the performance of presented approach , we present tests on different breast MRI images . The experimental results obtained , show that the overall accuracy offered by the employed machine learning techniques is high compared with other machine learning techniques including decision trees , rough sets , neural networks , and fuzzy artmap . To detect the caption from video sequence , we present algorithms based on fuzzy clustering neural networks . Since neural networks have the capabilities of learning and self-organizing and parallel computing mechanism , with the great increasing of digital images and video databases , neural networks based techniques become more efficient and popular tools for multimedia processing . Experimental results show that our caption detection scheme is effective and robust . A neural network image analysis systems ( NNIAS ) was developed by coordinating several networks . This neural network systems approach successfully identified and located objects in digitized images . This system of networks performed better than single large or small networks and surpassed human performance.<<ETX > > iIt I Modelling time-dependent nonlinear systems isatopic ofgrowing interest inneural networks . Promising results havebeenobtained forthecapabilities ofrecurrentnetworks , andtime-day networks , butfewresults havebeenobtained forthetheoretical capabilities of these structures . We also argue here that the controversial subject of direct vision plays a crucial role in natural vision , and we attempt to relate this to the model . The recognition of massive parallelism in natural vision has led to proposals for emulating aspects of neural networks in technology . The systems model developed in this work demonstrates software-simulated cellular automata ( CAs ) in the role of mainly low-level image processing . The neural predistorter utilizes gradient algorithms for its adaptation . Our results indicate clear improvements in performance for neural networks networks incorporating memory into their structure In this paper , we use this corpus to evaluate a variety of models for natural language inference , including rule - based systems , simple linear classifiers , and neural network - based models . We find that two models achieve comparable performance : a feature - rich classifier model and a neural network model centered around a Long Short - Term Memory network ( LSTM ; ? ) . We further evaluate the LSTM model by taking advantage of its ready support for transfer learning , and show that it can be adapted to an existing NLI challenge task , yielding the best reported performance by a neural network model and approaching the overall state of the art . In the present thesis , this challenge is addressed by using representation learning , which has been one of the most important innovations in machine learning in the last decade . Representation learning is the basis for modern approaches to natural language processing and artificial neural networks , in particular deep learning , which includes popular models such as convolutional neural networks ( CNN ) and recurrent neural networks ( RNN ) . It has also been shown that many approaches to tensor decomposition and multi-way models can also be related to representation learning . However , most of these methods only focus on flame detection , thus they are still not sufficient to prevent loss of life and reduce economic and material damage . To tackle this issue , we present a deep learningbased method for detecting wildfires at an early stage by identifying flames and smokes at once . To realize the proposed idea , a large dataset for wildfire is acquired from the web . They span computational and empirical approaches , and allow for critical contact with other theoret- ical perspectives . The recent rapid progress on deep learn- ing within the machine learning community , and the growing number of deep learning-based models in the cognitive sci- The goal of the workshop is to explore the relevance of re- cent deep learning advances to cognition , to bring together cognitive science-oriented deep learning researchers , and to facilitate exchanges between the machine learning and cog- nitive science communities . While deep learning has been a persistent thread of re- search in the cognitive sciences from the very beginning , a goal of the workshop is to provide a focal point for this com- munity and a forum for important discussions and collabora- tions that can span methodological approaches . In this paper , we focus on deep learning method to achieve high precision in stock market forecast . And a deep belief networks(DBNs ) , which is a kind of deep learning algorithm model , coupled with stock technical indicators(STIs ) and two-dimensional principal component analysis((2D)2PCA ) is introduced as a novel approach to predict the closing price of stock market . A comparison experiment is also performed to evaluate this model . By interviewing undergraduates with different professional backgrounds , this study will conduct deep observation from the perspective of the individual , to understand how undergraduates invoke learning strategies during network learning . The research results have showed that learners can basically complete shallow level in multiple dimensions and expand some deep level learning , but the degree of deep learning needs to be improved . Finally , we evaluate our deep model on PASCAL VOC2012 benchmark dataset , extensive experiments show that our DBM-based model enables to learn useful hierarchical feature representations from few training samples . Specifically , by feeding the DBM-based deep network with prior-described features such as Log-Gabor , HoG and Gist , we get a significant improvement on performance in comparison with the state-of-art results and the best performance is achieved by using a fusion form of the three descriptors as source material for learning deep model . Furthermore , experiments with prior-extracted features as input layer demonstrate that the deep learning networks have provided a perspective of potential ability to learn critical representation of given data , and have presented favorable vehicle recognition performance . for example , Although the current face recognition algorithm can achieve a recognition rate of 99.9 % in an ordinary indoor , the face recognition of criminal investigation is less than 50 % . Deep Learning Deep Learning [ 2 - 3 ] originates from the research of artificial neural networks and is a new field in machine learning research . Data is abstracted at a high level by using complex structures or the multiple processing layers of multiple nonlinear transformations . The ultrasound scan does not only display bone specimens , but also contains muscles , soft tissue , and other parts under the skin tissue Therefore we need a system that can automatically recognize bone specimens in ultrasound images . This study implements deep learning based learning systems using the convolutional neural network ( CNN ) method with YOLOv3 . The training results from the network detector with IoU threshold 0.5 can recognize bone objects in mAP@50 , mAP@75 and mAP@50:95 with values of 99.98 , 97.68 and 85.67 respectively . Learning in deep models using Bayesian methods has generated significant attention recently . This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference , while maintaining a measure of uncertainty in the model parameters . section : Introduction Deep learning models have achieved great success in visual recognition tasks . However , these supervised learning models need large amounts of labelled data and many iterations to train their large number of parameters . In , Glasner et al . begin to use the image multi - scale information for single image SR and obtain state - of - the - art results . Recently , due to the availability of much larger training dataset and the powerful GPU implementation , deep learning based methods achieve great success in many fields , including both high level and low level computer vision problems . Look through the literature , most state - of - the - art single image SR methods are based on deep learning . Future extensions of node2vec could involve networks with special structure such as heterogeneous information networks , networks with explicit domain features for nodes and edges and signed - edge networks . Continuous feature representations are the backbone of many deep learning algorithms , and it would be interesting to use node2vec representations as building blocks for end - to - end deep learning on graphs . Acknowledgements . document : DR - BiLSTM : Dependent Reading Bidirectional LSTM for Natural Language Inference We present a novel deep learning architecture to address the natural language inference ( NLI ) task . Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis . Each hidden layer is a fully - connected layer that computes the following transformation : where and respectively denote the weight matrix and the bias term corresponding to the hidden layer , and is the activation function . We use the rectifier linear unit as the activation function , which is a common choice in the deep learning literature Lecun:2015 . The output layer is a fully - connected layer with a single continuous output . Note that this is equivalent to applying a softmax function over the unary potentials U across all the labels at each pixel . The softmax function has been extensively used in CNN architectures before and is therefore well known in the deep learning community . This operation does not include any parameters and the error differentials received at the output of the step during back - propagation could be passed down to the unary potential inputs after performing usual backward pass calculations of the softmax transformation . Graph Convolutional Neural Networks ( GCNNs ) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi - cross - domains including bioinformatics , chemoinformatics , social networks , natural language processing and computer vision . In this paper , we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in and propose our Graph Capsule Network ( GCAPS - CNN ) model . On the other hand , many researchers have found convolutional networks ( ConvNets ) are useful in extracting information from raw signals , ranging from computer vision applications to speech recognition and others . In particular , time - delay networks used in the early days of deep learning research are essentially convolutional networks that model sequential data . In this article we explore treating text as a kind of raw signal at character level , and applying temporal ( one - dimensional ) ConvNets to it . This momentum sequence parameterizes the original image sequence in the LDDMM framework and lies in the tangent space of the baseline image , which is Euclidean . A recurrent network with long term-short memory ( LSTM ) units encodes the time-varying changes in the vector-momentum sequence , and a convolutional neural network ( CNN ) encodes the baseline image of the vector momenta . Features extracted by the LSTM and CNN are fed into a decoder network to reconstruct the vector momentum sequence , which is used for the image sequence prediction by deforming the baseline image with LDDMM shooting . document : A Discriminatively Learned CNN Embedding for Person Re - identification In this paper , we revisit two popular convolutional neural networks ( CNN ) in person re - identification ( re - ID ) , i.e. , verification and identification models . The two models have their respective advantages and limitations due to different loss functions . Table 1 summarizes the results of our models under different settings . We will compare the following models : Faster R - CNN [ reference ] , R 2 CNN - 1 , R 2 CNN - 2 , R 2 CNN - 3 , R 2 CNN - 4 , and R 2 CNN - 5 . We mainly focus on evaluating the influence of the axis - aligned box regression ( 1 ) and the inclined box regression ( 2 ) , the influence of anchor scales and NMS strategy , and the impact of different pooled sizes of ROIPoolings . The main contributions of this paper are summarized in the following : We propose a simple yet effective and scalable deep CNN framework for SISR . The proposed model goes beyond the widely - used bicubic degradation assumption and works for multiple and even spatially variant degradations , thus making a substantial step towards developing a practical CNN - based super - resolver for real applications . We propose a novel dimensionality stretching strategy to address the dimensionality mismatch between LR input image , blur kernel and noise level . Following SSD , we also use normalization layer on conv3_3 , conv4_3 , and conv5_3 and initialize the norm 40 . For Fast R - CNN part , we extract features from conv3_3 , conv5_3 , and conv8 for each region proposal and concatenate all the features to predict class scores and further adjust the proposals . We train this detector from COCO dataset with input image and have achieved 35.5 mAP on the COCO minival dataset , with only 10ms processing time for a image on a single GPU . We use ResNet - 50 [ reference ] to implement the CNN regressor . The encoder contains 6 convolutional layers , each followed by a ReLU and a max pooling layer . LiteFlowNet - ft outperforms FlowNet2 - ft - sintel and EpicFlow for Sintel final testing set . Despite DC Flow ( a hybrid method consists of CNN and post - processing ) performs better than LiteFlowNet , its GPU runtime requires several seconds that makes it formidable in many applications . Figure [ reference ] shows some examples of flow fields on Sintel dataset . 820 M versus a total of 1.04B parameters . In one of the experiments we froze the word - LSTM after convergence and replaced the Softmax layer with the CNN Softmax sub - network . Without any fine - tuning that model was able to reach 39.8 perplexity with only 293 M weights ( as seen in Table [ reference ] ) . Abstract In this paper , we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles ( SSs ) in a multi-channel electroencephalographic signal . In the first stage , a discrete perceptron is used to eliminate definite non-SSs . In the present study , an artificial neural networks-based model ( ANNs ) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels . Fourteen parameters affecting the Vickers microhardness were considered as inputs , including the austenitizing temperature , cooling rate , initial austenite grain size , different chemical compositions and Nb in solution . Artificial neural network Artificial Neural Networks ( ANN ) has the characteristics of adaptive , self-organization and self-learning . It can obtain the capabilities such as classify knowledge , pattern discrimination and associative memory by training and learning . This fault tolerance technique can also be suitably used for hardware implementation of artificial neural networks along with other redundancy methods . Keywords — Fault tolerance , Artificial Neural Network neural network , sigmoid function , stuck-at-faults , VLSI realization . The second proposed PV fault detection algorithm detects defective bypass diodes in PV modules using Mamdani Fuzzy Logic . Whereas , a third PV detection algorithm is based on artificial neural networks ( ANN ) networks . Four different ANN models have been modelled , which can be classified as follows : - 2 inputs , 5 outputs using 1 hidden layer - 2 inputs , 5 outputs using 2 hidden layers - 2 inputs , 9 outputs using 1 hidden layer - 2 inputs , 9 outputs using 2 hidden layers The output results for the last ANN network had the highest overall fault detection accuracy of 92.1 % . In this paper we consider five well known approaches for function approximation . Specifically we target polynomial approximation , general additive models ( Gam ) , local regression ( Loess ) , multivariate additive regression splines ( Mars ) and artificial neural networks (Ann).Neural networks can be viewed as models of real systems , built by tuning parameters known as weights . In training the net , the problem is to find the weights that optimize its performance ( i.e. to minimize the error over the training set ) . FD may alsobeapplied tograyscale images , butwe consider color images because they include color information which helps us toincrease detection speedusing segmentation methods . Theartificial neural networks becomepopular tosolve FR andFDproblems . Allelements ofneural network (NN)can worksimultaneously . Recently there has been a resurgence in models of computation using explicit storage and a notion of attention ; manipulating such a storage offers an approach to both of these challenges . In , the storage is endowed with a continuous representation ; reads from and writes to the storage , as well as other processing steps , are modeled by the actions of neural networks . In this work , we present a novel recurrent neural network ( RNN ) architecture where the recurrence reads from a possibly large external memory multiple times before outputting a symbol . Websites of high results are referred to as authoritative sites under the search query , thus providing a new perspective to measure website authoritativeness . By comparing the three model experiments with Word2vec , CNN and LSTM , the experimental results on open datasets show that it is effective to use these three models , of which the LSTM model works best . The first type uses an alignment function based on “ concatenation ” of the input and each candidate ( we use the term “ concatenation ” as described ) , and the second type uses an alignment function based on the dot product of the input and each candidate . The “ concatenation ” alignment function adds one input vector ( e.g. hidden state vector of the LSTM ) to each candidate feature vector , embeds the resulting vectors into scalar values , and then applies the softmax function to generate the attention weight for each candidate . use the “ concatenation ” alignment function in their soft attention models and gives a literature review of such models applied to different tasks . This operation is similar as the bottleneck structure in , but more efficient . After that , Conv - LSTM layers with 256 channels are inserted into the single - shot TLL network . We try to incrementally stack Conv - LSTM layers to the network , however , due to difficulties in training multiple RNNs , experiments show that stacking two or more Conv - LSTMs is not beneficial . The code is available at the github repository . We use the LSTM variant implemented in PyCNN , and optimize using the Adam optimizer . Unless otherwise noted , we use the default values provided by PyCNN ( e.g. for random initialization , learning rates etc ) . Based on this , we proceeded with three hidden layers , with 1000 hidden units each . The perplexities of each LSTM - LM we used in the final combination ( before interpolating with the N - gram model ) can be found in Table [ reference ] . For the final system , we interpolated two LSTM - LMs with an N - gram LM for the forward - direction LM , and similarly for the backward - direction LM . Character - level language modelling is a representative example of discrete [ reference ] 1.67 MRNN [ reference ] 1.60 GF - LSTM [ reference ] 1.58 Grid - LSTM [ reference ] 1.47 MI - LSTM 1.44 Recurrent Memory Array Structures ( Rocki , 2016a ) 1.40 SF - LSTM In previous work , 3D - CNN is applied in HSI classification and obtained a good behavior . For RNNs , Convolutional - LSTM ( CLSTM ) also achieved a good performance in HSI classification . 3D - CNNs and CLSTM consider both spatial contextual information and spectral continuity , which result in a high accuracy . Then , assuming we have layers of biLSTM , the final vector representation will be obtained by applying row - max - pool over the output of the last biLSTM layer , similar to conneau2017supervised . The final layer is defined as : where , , is the dimension of the hidden state of the last forward and backward LSTM layers , and is the final vector representation for the source sentence ( which is later fed to the NLI classifier ) . The closest encoder architecture to ours is that of conneau2017supervised , whose model consists of a single - layer biLSTM with a max - pooling layer , which we treat as our starting point . The Swedish architect and theorist Elias Cornell defined architecture as ' The aesthetic organization of practical reality ' . The English Arts and Crafts architect and theorist W. Lethaby wrote in 1891 : ' ... so is building but the vehicle of architecture which is the thought behind form , embodied and realized for the purpose of its manifestation and transmission ' . Or we call that architecture is the built environment with aesthetically and spiritually stimulating space and form . do any image preprocessing , except dividing images by 255 to provide them in [ 0 , 1 ] range as input . All of our experiments except ImageNet are based on architecture with pre - activation residual blocks and we use it as baseline . For ImageNet , we find that using pre - activation in networks with less than 100 layers does not make any significant difference subsection : Using biLMs for supervised NLP tasks Given a pre - trained biLM and a supervised architecture for a target NLP task , it is a simple process to use the biLM to improve the task model . We simply run the biLM and record all of the layer representations for each word . 4 ) We found best results with an RNNLM configuration that had a second , non - recurrent hidden layer . This produced lower perplexity and word error than the standard , single - hidden - layer RNNLM architecture . The overall network architecture thus had two hidden layers with 1000 units each , using ReLU nonlinearities . For example , in our real system we need to predict hundreds of ads for each visitor in less than 10 milliseconds . In our practice , several important techniques are deployed for accelerating online serving of industrial deep networks under the CPU - GPU architecture : i ) request batching which merges adjacent requests from CPU to take advantage of GPU power , ii ) GPU memory optimization which improves the access pattern to reduce wasted transactions in GPU memory , iii ) concurrent kernel computation which allows execution of matrix computations to be processed with multiple CUDA kernels concurrently . The branches replace the multiple heads in the attention mechanism of the original Transformer network , and the model learns to combine these branches during training . This branched architecture enables the network to achieve comparable performance at a significantly lower computational cost . Indeed , through this modification , we improve the state - of - the - art performance by and BLEU scores on the WMT 2014 English - to - German and English - to - French tasks , respectively . NMT models parameterize with an encoder neural network which reads the source sentence and a decoder neural network which produces a distribution over the target sentence ( one word at a time ) given the source . We employ the attentional architecture from Luong2015 , which achieved state - of - the - art results on English German translation . subsection : Knowledge Distillation The DeepLab - ASPP - L model has four parallel branches after conv5 block of ResNet101 architecture . Each branch is a convolutional layer , with a different atrous rate ( ) . section : Conclusion In this work we presented a new off - policy agent based on Retrace actor - critic architecture and show that it achieves similar performance as the current state - of - the - art while giving significant real - time performance gains . We demonstrate the benefits of each of the suggested algorithmic improvements , including Distributional Retrace , beta - LOO policy gradient and contextual priority tree . In this paper , we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks ( CNNs ) , which have had many successes in visual recognition tasks . For learning such saliency models , we introduce a neural network architecture , which has fully connected layers on top of CNNs responsible for feature extraction at three different scales . We then propose a refinement method to enhance the spatial coherence of our saliency results . The peculiar features such as texture and color are used for exhibiting the content of an image . In this paper , we introduce a Wavelet and Gabor based Image Retrieval ( WAGBIR ) system , a Neural Network based architecture for content based image retrieval . This technique uses wavelet transform and Gabor filters features for repository image representation . It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods . However , a new challenge of neural network architecture design has also attracted attention : due to the high dimension of fMRI volume images , the manual process of network model design is very time-consuming and error prone . To tackle this problem , we proposed a Particle Swarm Optimization ( PSO ) based neural architecture search ( NAS ) framework for a deep belief network ( DBN ) that models volumetric fMRI data , named NAS-DBN . The efficacy of self-attention in speech applications , however , seems not fully blown yet since it is challenging to handle highly correlated speech frames in the context of self-attention . In this paper we propose a new neural network model architecture , namely multi-stream self-attention , to address the issue thus make the self-attention mechanism more effective for speech recognition . The proposed model architecture consists of parallel streams of self-attention encoders , and each stream has layers of 1D convolutions with dilated kernels whose dilation rates are unique given stream , followed by a self-attention layer . U-Nets have been established as a standard neural network design architecture for image-to-image learning problems such as segmentation and inverse problems in imaging . For high-dimensional applications , as they for example appear in 3D medical imaging , U-Nets however have prohibitive memory requirements . section : Conclusions We introduced a neural network architecture and training procedure for learning compact representations of image sets for template - based face recognition . Due to the novel GhostVLAD layer , the network is able to automatically learn to weight face descriptors depending on their information content . The database has its own advantages where the quality of images is high and segregation has been done for various expressions of newborn . The Deep CNN applied in this paper is more advantageous when compared to regular MLP . Along with this the results taken from application of proposed technique have been compared to state-of-the-art technique applied on the same database and it shows improved results . We use various preprocessing and feature extraction methods and classify using the artificial neural network ( ANN ) and deep convolutional neural network ( CNN ) . We compare their performance and find that the best results were achieved using a deep CNN architecture . This work serves as a proof of concept and can be further extended to outdoor on-field test cases . We incorporate our proposed algorithm into general FR pipeline and achieve encouraging identification results on the CMU-PIE , FERET , and LFW datasets , compared to previously developed methods . In addition , the feasibility of our method has been successfully demonstrated by making comparisons with other state-of-the-art FR methods ( including deep CNN based method ) under the FERET and FRGC 2.0 evaluation protocols . Based on results , our method is advantageous in terms of high recognition accuracy and low complexity , as well as straightforward implementation . The earliest attempts to apply neural networks to graphs are due to [ reference ] ( see more recent formulation [ reference ][ reference ] ) . [ reference ][ reference ] formulated CNN - like deep neural architectures on graphs in the spectral domain , employing the analogy between the classical Fourier transforms and projections onto the eigenbasis of the graph Laplacian operator [ reference ] . In a follow - up work , proposed an efficient filtering scheme using recurrent Chebyshev polynomials , which reduces the complexity of CNNs on graphs to the same complexity of standard CNNs ( on grids ) . Mean absolute error ( MAE ) , root mean square error ( RMSE ) , and normalized mean square error are introduced to evaluate the performance of predictive results . Deep Bi-LSTM has great performance in the cases with relative long latency , average MAE of 0.074 mm , RMSE of 0.097 mm , and normalized root mean square error ( nRMSE ) of 0.081 with latency about 400 ms are obtained from predictive results of Deep Bi-LSTM . It demonstrates that the prediction accuracy of our proposed Deep Bi-LSTM is about five times better than traditional autoregressive integrated moving average model and about three times better than adaptive boosting and multi-layer perceptron neural network when the latency of 400 ms . It achieves to predict a result in every 0.006 seconds with a short detection duration ( one second ) . By comparing with other two types deep learning approaches ( DCNN and LSTM ) , the presented deep C-LSTM obtains the best performance for classifying these five classes . All of the obtained total accuracy are over 98.80 % . In order to use this architecture for error detection , we create a bidirectional LSTM , making use of the advanced features of LSTM and incorporating context on both sides of the target token . In addition , we experiment with a deep bidirectional LSTM , which includes two consecutive layers of bidirectional LSTMs , modeling even more complex features and performing multiple passes through the sentence . For comparison with non - neural models , we also report results using CRFs , which are a popular choice for sequence labeling tasks .
To evaluate the performance of presented approach , we present tests on different breast MRI images . The experimental results obtained , show that the overall accuracy offered by the employed machine learning techniques is high compared with other machine learning techniques including decision trees , rough sets , neural networks , and fuzzy artmap .
To detect the caption from video sequence , we present algorithms based on fuzzy clustering neural networks . Since neural networks have the capabilities of learning and self-organizing and parallel computing mechanism , with the great increasing of digital images and video databases , neural networks based techniques become more efficient and popular tools for multimedia processing . Experimental results show that our caption detection scheme is effective and robust .
A neural network image analysis systems ( NNIAS ) was developed by coordinating several networks . This neural network systems approach successfully identified and located objects in digitized images . This system of networks performed better than single large or small networks and surpassed human performance.<<ETX > >
iIt I Modelling time-dependent nonlinear systems isatopic ofgrowing interest inneural networks . Promising results havebeenobtained forthecapabilities ofrecurrentnetworks , andtime-day networks , butfewresults havebeenobtained forthetheoretical capabilities of these structures .
We also argue here that the controversial subject of direct vision plays a crucial role in natural vision , and we attempt to relate this to the model . The recognition of massive parallelism in natural vision has led to proposals for emulating aspects of neural networks in technology . The systems model developed in this work demonstrates software-simulated cellular automata ( CAs ) in the role of mainly low-level image processing .
The neural predistorter utilizes gradient algorithms for its adaptation . Our results indicate clear improvements in performance for neural networks networks incorporating memory into their structure
In this paper , we use this corpus to evaluate a variety of models for natural language inference , including rule - based systems , simple linear classifiers , and neural network - based models . We find that two models achieve comparable performance : a feature - rich classifier model and a neural network model centered around a Long Short - Term Memory network ( LSTM ; ? ) . We further evaluate the LSTM model by taking advantage of its ready support for transfer learning , and show that it can be adapted to an existing NLI challenge task , yielding the best reported performance by a neural network model and approaching the overall state of the art .
In the present thesis , this challenge is addressed by using representation learning , which has been one of the most important innovations in machine learning in the last decade . Representation learning is the basis for modern approaches to natural language processing and artificial neural networks , in particular deep learning , which includes popular models such as convolutional neural networks ( CNN ) and recurrent neural networks ( RNN ) . It has also been shown that many approaches to tensor decomposition and multi-way models can also be related to representation learning .
However , most of these methods only focus on flame detection , thus they are still not sufficient to prevent loss of life and reduce economic and material damage . To tackle this issue , we present a deep learningbased method for detecting wildfires at an early stage by identifying flames and smokes at once . To realize the proposed idea , a large dataset for wildfire is acquired from the web .
They span computational and empirical approaches , and allow for critical contact with other theoret- ical perspectives . The recent rapid progress on deep learn- ing within the machine learning community , and the growing number of deep learning-based models in the cognitive sci- The goal of the workshop is to explore the relevance of re- cent deep learning advances to cognition , to bring together cognitive science-oriented deep learning researchers , and to facilitate exchanges between the machine learning and cog- nitive science communities . While deep learning has been a persistent thread of re- search in the cognitive sciences from the very beginning , a goal of the workshop is to provide a focal point for this com- munity and a forum for important discussions and collabora- tions that can span methodological approaches .
In this paper , we focus on deep learning method to achieve high precision in stock market forecast . And a deep belief networks(DBNs ) , which is a kind of deep learning algorithm model , coupled with stock technical indicators(STIs ) and two-dimensional principal component analysis((2D)2PCA ) is introduced as a novel approach to predict the closing price of stock market . A comparison experiment is also performed to evaluate this model .
By interviewing undergraduates with different professional backgrounds , this study will conduct deep observation from the perspective of the individual , to understand how undergraduates invoke learning strategies during network learning . The research results have showed that learners can basically complete shallow level in multiple dimensions and expand some deep level learning , but the degree of deep learning needs to be improved .
Finally , we evaluate our deep model on PASCAL VOC2012 benchmark dataset , extensive experiments show that our DBM-based model enables to learn useful hierarchical feature representations from few training samples . Specifically , by feeding the DBM-based deep network with prior-described features such as Log-Gabor , HoG and Gist , we get a significant improvement on performance in comparison with the state-of-art results and the best performance is achieved by using a fusion form of the three descriptors as source material for learning deep model . Furthermore , experiments with prior-extracted features as input layer demonstrate that the deep learning networks have provided a perspective of potential ability to learn critical representation of given data , and have presented favorable vehicle recognition performance .
for example , Although the current face recognition algorithm can achieve a recognition rate of 99.9 % in an ordinary indoor , the face recognition of criminal investigation is less than 50 % . Deep Learning Deep Learning [ 2 - 3 ] originates from the research of artificial neural networks and is a new field in machine learning research . Data is abstracted at a high level by using complex structures or the multiple processing layers of multiple nonlinear transformations .
The ultrasound scan does not only display bone specimens , but also contains muscles , soft tissue , and other parts under the skin tissue Therefore we need a system that can automatically recognize bone specimens in ultrasound images . This study implements deep learning based learning systems using the convolutional neural network ( CNN ) method with YOLOv3 . The training results from the network detector with IoU threshold 0.5 can recognize bone objects in mAP@50 , mAP@75 and mAP@50:95 with values of 99.98 , 97.68 and 85.67 respectively .
Learning in deep models using Bayesian methods has generated significant attention recently . This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference , while maintaining a measure of uncertainty in the model parameters .
section : Introduction Deep learning models have achieved great success in visual recognition tasks . However , these supervised learning models need large amounts of labelled data and many iterations to train their large number of parameters .
In , Glasner et al . begin to use the image multi - scale information for single image SR and obtain state - of - the - art results . Recently , due to the availability of much larger training dataset and the powerful GPU implementation , deep learning based methods achieve great success in many fields , including both high level and low level computer vision problems . Look through the literature , most state - of - the - art single image SR methods are based on deep learning .
Future extensions of node2vec could involve networks with special structure such as heterogeneous information networks , networks with explicit domain features for nodes and edges and signed - edge networks . Continuous feature representations are the backbone of many deep learning algorithms , and it would be interesting to use node2vec representations as building blocks for end - to - end deep learning on graphs . Acknowledgements .
document : DR - BiLSTM : Dependent Reading Bidirectional LSTM for Natural Language Inference We present a novel deep learning architecture to address the natural language inference ( NLI ) task . Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis .
Each hidden layer is a fully - connected layer that computes the following transformation : where and respectively denote the weight matrix and the bias term corresponding to the hidden layer , and is the activation function . We use the rectifier linear unit as the activation function , which is a common choice in the deep learning literature Lecun:2015 . The output layer is a fully - connected layer with a single continuous output .
Note that this is equivalent to applying a softmax function over the unary potentials U across all the labels at each pixel . The softmax function has been extensively used in CNN architectures before and is therefore well known in the deep learning community . This operation does not include any parameters and the error differentials received at the output of the step during back - propagation could be passed down to the unary potential inputs after performing usual backward pass calculations of the softmax transformation .
Graph Convolutional Neural Networks ( GCNNs ) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi - cross - domains including bioinformatics , chemoinformatics , social networks , natural language processing and computer vision . In this paper , we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in and propose our Graph Capsule Network ( GCAPS - CNN ) model .
On the other hand , many researchers have found convolutional networks ( ConvNets ) are useful in extracting information from raw signals , ranging from computer vision applications to speech recognition and others . In particular , time - delay networks used in the early days of deep learning research are essentially convolutional networks that model sequential data . In this article we explore treating text as a kind of raw signal at character level , and applying temporal ( one - dimensional ) ConvNets to it .
This momentum sequence parameterizes the original image sequence in the LDDMM framework and lies in the tangent space of the baseline image , which is Euclidean . A recurrent network with long term-short memory ( LSTM ) units encodes the time-varying changes in the vector-momentum sequence , and a convolutional neural network ( CNN ) encodes the baseline image of the vector momenta . Features extracted by the LSTM and CNN are fed into a decoder network to reconstruct the vector momentum sequence , which is used for the image sequence prediction by deforming the baseline image with LDDMM shooting .
document : A Discriminatively Learned CNN Embedding for Person Re - identification In this paper , we revisit two popular convolutional neural networks ( CNN ) in person re - identification ( re - ID ) , i.e. , verification and identification models . The two models have their respective advantages and limitations due to different loss functions .
Table 1 summarizes the results of our models under different settings . We will compare the following models : Faster R - CNN [ reference ] , R 2 CNN - 1 , R 2 CNN - 2 , R 2 CNN - 3 , R 2 CNN - 4 , and R 2 CNN - 5 . We mainly focus on evaluating the influence of the axis - aligned box regression ( 1 ) and the inclined box regression ( 2 ) , the influence of anchor scales and NMS strategy , and the impact of different pooled sizes of ROIPoolings .
The main contributions of this paper are summarized in the following : We propose a simple yet effective and scalable deep CNN framework for SISR . The proposed model goes beyond the widely - used bicubic degradation assumption and works for multiple and even spatially variant degradations , thus making a substantial step towards developing a practical CNN - based super - resolver for real applications . We propose a novel dimensionality stretching strategy to address the dimensionality mismatch between LR input image , blur kernel and noise level .
Following SSD , we also use normalization layer on conv3_3 , conv4_3 , and conv5_3 and initialize the norm 40 . For Fast R - CNN part , we extract features from conv3_3 , conv5_3 , and conv8 for each region proposal and concatenate all the features to predict class scores and further adjust the proposals . We train this detector from COCO dataset with input image and have achieved 35.5 mAP on the COCO minival dataset , with only 10ms processing time for a image on a single GPU .
We use ResNet - 50 [ reference ] to implement the CNN regressor . The encoder contains 6 convolutional layers , each followed by a ReLU and a max pooling layer .
LiteFlowNet - ft outperforms FlowNet2 - ft - sintel and EpicFlow for Sintel final testing set . Despite DC Flow ( a hybrid method consists of CNN and post - processing ) performs better than LiteFlowNet , its GPU runtime requires several seconds that makes it formidable in many applications . Figure [ reference ] shows some examples of flow fields on Sintel dataset .
820 M versus a total of 1.04B parameters . In one of the experiments we froze the word - LSTM after convergence and replaced the Softmax layer with the CNN Softmax sub - network . Without any fine - tuning that model was able to reach 39.8 perplexity with only 293 M weights ( as seen in Table [ reference ] ) .
Abstract In this paper , we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles ( SSs ) in a multi-channel electroencephalographic signal . In the first stage , a discrete perceptron is used to eliminate definite non-SSs .
In the present study , an artificial neural networks-based model ( ANNs ) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels . Fourteen parameters affecting the Vickers microhardness were considered as inputs , including the austenitizing temperature , cooling rate , initial austenite grain size , different chemical compositions and Nb in solution .
Artificial neural network Artificial Neural Networks ( ANN ) has the characteristics of adaptive , self-organization and self-learning . It can obtain the capabilities such as classify knowledge , pattern discrimination and associative memory by training and learning .
This fault tolerance technique can also be suitably used for hardware implementation of artificial neural networks along with other redundancy methods . Keywords — Fault tolerance , Artificial Neural Network neural network , sigmoid function , stuck-at-faults , VLSI realization .
The second proposed PV fault detection algorithm detects defective bypass diodes in PV modules using Mamdani Fuzzy Logic . Whereas , a third PV detection algorithm is based on artificial neural networks ( ANN ) networks . Four different ANN models have been modelled , which can be classified as follows : - 2 inputs , 5 outputs using 1 hidden layer - 2 inputs , 5 outputs using 2 hidden layers - 2 inputs , 9 outputs using 1 hidden layer - 2 inputs , 9 outputs using 2 hidden layers The output results for the last ANN network had the highest overall fault detection accuracy of 92.1 % .
In this paper we consider five well known approaches for function approximation . Specifically we target polynomial approximation , general additive models ( Gam ) , local regression ( Loess ) , multivariate additive regression splines ( Mars ) and artificial neural networks (Ann).Neural networks can be viewed as models of real systems , built by tuning parameters known as weights . In training the net , the problem is to find the weights that optimize its performance ( i.e. to minimize the error over the training set ) .
FD may alsobeapplied tograyscale images , butwe consider color images because they include color information which helps us toincrease detection speedusing segmentation methods . Theartificial neural networks becomepopular tosolve FR andFDproblems . Allelements ofneural network (NN)can worksimultaneously .
Recently there has been a resurgence in models of computation using explicit storage and a notion of attention ; manipulating such a storage offers an approach to both of these challenges . In , the storage is endowed with a continuous representation ; reads from and writes to the storage , as well as other processing steps , are modeled by the actions of neural networks . In this work , we present a novel recurrent neural network ( RNN ) architecture where the recurrence reads from a possibly large external memory multiple times before outputting a symbol .
Websites of high results are referred to as authoritative sites under the search query , thus providing a new perspective to measure website authoritativeness . By comparing the three model experiments with Word2vec , CNN and LSTM , the experimental results on open datasets show that it is effective to use these three models , of which the LSTM model works best .
The first type uses an alignment function based on “ concatenation ” of the input and each candidate ( we use the term “ concatenation ” as described ) , and the second type uses an alignment function based on the dot product of the input and each candidate . The “ concatenation ” alignment function adds one input vector ( e.g. hidden state vector of the LSTM ) to each candidate feature vector , embeds the resulting vectors into scalar values , and then applies the softmax function to generate the attention weight for each candidate . use the “ concatenation ” alignment function in their soft attention models and gives a literature review of such models applied to different tasks .
This operation is similar as the bottleneck structure in , but more efficient . After that , Conv - LSTM layers with 256 channels are inserted into the single - shot TLL network . We try to incrementally stack Conv - LSTM layers to the network , however , due to difficulties in training multiple RNNs , experiments show that stacking two or more Conv - LSTMs is not beneficial .
The code is available at the github repository . We use the LSTM variant implemented in PyCNN , and optimize using the Adam optimizer . Unless otherwise noted , we use the default values provided by PyCNN ( e.g. for random initialization , learning rates etc ) .
Based on this , we proceeded with three hidden layers , with 1000 hidden units each . The perplexities of each LSTM - LM we used in the final combination ( before interpolating with the N - gram model ) can be found in Table [ reference ] . For the final system , we interpolated two LSTM - LMs with an N - gram LM for the forward - direction LM , and similarly for the backward - direction LM .
Character - level language modelling is a representative example of discrete [ reference ] 1.67 MRNN [ reference ] 1.60 GF - LSTM [ reference ] 1.58 Grid - LSTM [ reference ] 1.47 MI - LSTM 1.44 Recurrent Memory Array Structures ( Rocki , 2016a ) 1.40 SF - LSTM
In previous work , 3D - CNN is applied in HSI classification and obtained a good behavior . For RNNs , Convolutional - LSTM ( CLSTM ) also achieved a good performance in HSI classification . 3D - CNNs and CLSTM consider both spatial contextual information and spectral continuity , which result in a high accuracy .
Then , assuming we have layers of biLSTM , the final vector representation will be obtained by applying row - max - pool over the output of the last biLSTM layer , similar to conneau2017supervised . The final layer is defined as : where , , is the dimension of the hidden state of the last forward and backward LSTM layers , and is the final vector representation for the source sentence ( which is later fed to the NLI classifier ) . The closest encoder architecture to ours is that of conneau2017supervised , whose model consists of a single - layer biLSTM with a max - pooling layer , which we treat as our starting point .
The Swedish architect and theorist Elias Cornell defined architecture as ' The aesthetic organization of practical reality ' . The English Arts and Crafts architect and theorist W. Lethaby wrote in 1891 : ' ... so is building but the vehicle of architecture which is the thought behind form , embodied and realized for the purpose of its manifestation and transmission ' . Or we call that architecture is the built environment with aesthetically and spiritually stimulating space and form .
do any image preprocessing , except dividing images by 255 to provide them in [ 0 , 1 ] range as input . All of our experiments except ImageNet are based on architecture with pre - activation residual blocks and we use it as baseline . For ImageNet , we find that using pre - activation in networks with less than 100 layers does not make any significant difference
subsection : Using biLMs for supervised NLP tasks Given a pre - trained biLM and a supervised architecture for a target NLP task , it is a simple process to use the biLM to improve the task model . We simply run the biLM and record all of the layer representations for each word .
4 ) We found best results with an RNNLM configuration that had a second , non - recurrent hidden layer . This produced lower perplexity and word error than the standard , single - hidden - layer RNNLM architecture . The overall network architecture thus had two hidden layers with 1000 units each , using ReLU nonlinearities .
For example , in our real system we need to predict hundreds of ads for each visitor in less than 10 milliseconds . In our practice , several important techniques are deployed for accelerating online serving of industrial deep networks under the CPU - GPU architecture : i ) request batching which merges adjacent requests from CPU to take advantage of GPU power , ii ) GPU memory optimization which improves the access pattern to reduce wasted transactions in GPU memory , iii ) concurrent kernel computation which allows execution of matrix computations to be processed with multiple CUDA kernels concurrently .
The branches replace the multiple heads in the attention mechanism of the original Transformer network , and the model learns to combine these branches during training . This branched architecture enables the network to achieve comparable performance at a significantly lower computational cost . Indeed , through this modification , we improve the state - of - the - art performance by and BLEU scores on the WMT 2014 English - to - German and English - to - French tasks , respectively .
NMT models parameterize with an encoder neural network which reads the source sentence and a decoder neural network which produces a distribution over the target sentence ( one word at a time ) given the source . We employ the attentional architecture from Luong2015 , which achieved state - of - the - art results on English German translation . subsection : Knowledge Distillation
The DeepLab - ASPP - L model has four parallel branches after conv5 block of ResNet101 architecture . Each branch is a convolutional layer , with a different atrous rate ( ) .
section : Conclusion In this work we presented a new off - policy agent based on Retrace actor - critic architecture and show that it achieves similar performance as the current state - of - the - art while giving significant real - time performance gains . We demonstrate the benefits of each of the suggested algorithmic improvements , including Distributional Retrace , beta - LOO policy gradient and contextual priority tree .
In this paper , we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks ( CNNs ) , which have had many successes in visual recognition tasks . For learning such saliency models , we introduce a neural network architecture , which has fully connected layers on top of CNNs responsible for feature extraction at three different scales . We then propose a refinement method to enhance the spatial coherence of our saliency results .
The peculiar features such as texture and color are used for exhibiting the content of an image . In this paper , we introduce a Wavelet and Gabor based Image Retrieval ( WAGBIR ) system , a Neural Network based architecture for content based image retrieval . This technique uses wavelet transform and Gabor filters features for repository image representation .
It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods . However , a new challenge of neural network architecture design has also attracted attention : due to the high dimension of fMRI volume images , the manual process of network model design is very time-consuming and error prone . To tackle this problem , we proposed a Particle Swarm Optimization ( PSO ) based neural architecture search ( NAS ) framework for a deep belief network ( DBN ) that models volumetric fMRI data , named NAS-DBN .
The efficacy of self-attention in speech applications , however , seems not fully blown yet since it is challenging to handle highly correlated speech frames in the context of self-attention . In this paper we propose a new neural network model architecture , namely multi-stream self-attention , to address the issue thus make the self-attention mechanism more effective for speech recognition . The proposed model architecture consists of parallel streams of self-attention encoders , and each stream has layers of 1D convolutions with dilated kernels whose dilation rates are unique given stream , followed by a self-attention layer .
U-Nets have been established as a standard neural network design architecture for image-to-image learning problems such as segmentation and inverse problems in imaging . For high-dimensional applications , as they for example appear in 3D medical imaging , U-Nets however have prohibitive memory requirements .
section : Conclusions We introduced a neural network architecture and training procedure for learning compact representations of image sets for template - based face recognition . Due to the novel GhostVLAD layer , the network is able to automatically learn to weight face descriptors depending on their information content .
The database has its own advantages where the quality of images is high and segregation has been done for various expressions of newborn . The Deep CNN applied in this paper is more advantageous when compared to regular MLP . Along with this the results taken from application of proposed technique have been compared to state-of-the-art technique applied on the same database and it shows improved results .
We use various preprocessing and feature extraction methods and classify using the artificial neural network ( ANN ) and deep convolutional neural network ( CNN ) . We compare their performance and find that the best results were achieved using a deep CNN architecture . This work serves as a proof of concept and can be further extended to outdoor on-field test cases .
We incorporate our proposed algorithm into general FR pipeline and achieve encouraging identification results on the CMU-PIE , FERET , and LFW datasets , compared to previously developed methods . In addition , the feasibility of our method has been successfully demonstrated by making comparisons with other state-of-the-art FR methods ( including deep CNN based method ) under the FERET and FRGC 2.0 evaluation protocols . Based on results , our method is advantageous in terms of high recognition accuracy and low complexity , as well as straightforward implementation .
The earliest attempts to apply neural networks to graphs are due to [ reference ] ( see more recent formulation [ reference ][ reference ] ) . [ reference ][ reference ] formulated CNN - like deep neural architectures on graphs in the spectral domain , employing the analogy between the classical Fourier transforms and projections onto the eigenbasis of the graph Laplacian operator [ reference ] . In a follow - up work , proposed an efficient filtering scheme using recurrent Chebyshev polynomials , which reduces the complexity of CNNs on graphs to the same complexity of standard CNNs ( on grids ) .
Mean absolute error ( MAE ) , root mean square error ( RMSE ) , and normalized mean square error are introduced to evaluate the performance of predictive results . Deep Bi-LSTM has great performance in the cases with relative long latency , average MAE of 0.074 mm , RMSE of 0.097 mm , and normalized root mean square error ( nRMSE ) of 0.081 with latency about 400 ms are obtained from predictive results of Deep Bi-LSTM . It demonstrates that the prediction accuracy of our proposed Deep Bi-LSTM is about five times better than traditional autoregressive integrated moving average model and about three times better than adaptive boosting and multi-layer perceptron neural network when the latency of 400 ms .
It achieves to predict a result in every 0.006 seconds with a short detection duration ( one second ) . By comparing with other two types deep learning approaches ( DCNN and LSTM ) , the presented deep C-LSTM obtains the best performance for classifying these five classes . All of the obtained total accuracy are over 98.80 % .
In order to use this architecture for error detection , we create a bidirectional LSTM , making use of the advanced features of LSTM and incorporating context on both sides of the target token . In addition , we experiment with a deep bidirectional LSTM , which includes two consecutive layers of bidirectional LSTMs , modeling even more complex features and performing multiple passes through the sentence . For comparison with non - neural models , we also report results using CRFs , which are a popular choice for sequence labeling tasks .
Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks Caption detection from video sequence based on fuzzy neural networks A neural network systems approach to image processing 1992ACC1FM2 Representational Capabilities ofMultilayer Feedforwrard Networks withTime-Delay Synapses A Multiple-Systems Approach in the Symbolic Modelling of Human Vision Using neural network for reduction distrotion introduced by power amplifier in digital communication systems A large annotated corpus for learning natural language inference Learning representations for supervised information fusion using tensor decompositions and deep learning methods Early Wildfire Detection Using Convolutional Neural Network Workshop on Deep Learning and the Brain Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system The Application of Undergraduates' Deep Learning Strategy on Network Deep Boltzmann Machines based vehicle recognition Deep Learning-based New Methods of Images Processing for Criminal Investigation Human Bone Localization in Ultrasound Image Using YOLOv3 CNN Architecture High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models Learning to Compare: Relation Network for Few-Shot Learning Single image super-resolution with dilated convolution based multi-scale information learning inception module node2vec: Scalable Feature Learning for Networks DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Neural Ranking Models with Weak Supervision Conditional Random Fields as Recurrent Neural Networks Graph Capsule Convolutional Neural Networks Character-level Convolutional Networks for Text Classification Predictive Image Regression for Longitudinal Studies with Missing Data A Discriminatively Learned CNN Embedding for Person Reidentification R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Frustum PointNets for 3D Object Detection from RGB-D Data Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Exploring the Limits of Language Modeling Automatic recognition of sleep spindles in EEG by using artificial neural networks Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels Study on Mine Geological Hazard Assessment Model Based on ANN VLSI Realization of Artificial Neural Networks with Improved Fault Tolerance Fault detection and performance analysis of photovoltaic installations Approximating Unknown Mappings: An Experimental Evaluation ImprovedMethodofFaceDetection Using Color Images End-To-End Memory Networks Authoritative Prediction of Website Based on Deep Learning Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Achieving Human Parity in Conversational Speech Recognition Hierarchical Multiscale Recurrent Neural Networks Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification Shortcut-Stacked Sentence Encoders for Multi-Domain Inference Architectural photography: photography, as a method of appreciation of three dimensional architectural phenomina by two dimensional visual language Wide Residual Networks Deep contextualized word representations The microsoft 2016 conversational speech recognition system Deep Interest Network for Click-Through Rate Prediction Weighted Transformer Network for Machine Translation Sequence-Level Knowledge Distillation Revisiting Unreasonable Effectiveness of Data in Deep Learning Era The Reactor: A Sample-Efficient Actor-Critic Architecture Visual saliency based on multiscale deep features WAGBIR: Wavelet and Gabor Based Image Retrieval Technique for the Spatial-Color and Texture Feature Extraction Using BPN in Multimedia Database Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention with Dilated 1D Convolutions iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling GhostVLAD for set-based face recognition Newborn face recognition using deep convolutional neural network Application of Machine Learning to Evaluate Insulator Surface Erosion Spatial pyramid face feature representation and weighted dissimilarity matching for improved face recognition Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks A Feasibility of Respiration Prediction Based on Deep Bi-LSTM for Real-Time Tumor Tracking Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals Compositional Sequence Labeling Models for Error Detection in Learner Writing
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EMNLP IW-FCV AAAI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Image Processing (ICIP) KDD NAACL-HLT SIGIR 2015 IEEE International Conference on Computer Vision (ICCV) ArXiv NIPS ACM Trans. Multim. Comput. Commun. Appl. ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv NIPS BigDataService ECCV ArXiv Transactions of the Association for Computational Linguistics ArXiv ICLR ArXiv RepEval@EMNLP BMVC NAACL-HLT 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) KDD ArXiv EMNLP 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv CVPR MMMI@MICCAI ACCV NIPS ACL
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