ECG(Electrocardiogram ) , a field of Bio-signal , is generally experimented with classification algorithms most of which are SVM(Support Vector Machine ) , MLP(Multilayer Perceptron ) . But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm . The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed . This new ANN architecture tackles the problem of context dependent behaviour training . Roughly , the principle is based on main MLP ( Multilayered Perceptron ) in which each synaptic weight connection value is estimated by another MLP ( an OWE ) with respect to context representation . In this paper , we present 2 hybrid systems for phoneme recognition . For the Aurora2 noisy digit recognition task , the use of phase sensitive filters improved the MFCC baseline , whereas using filters that neglect phase information did not . While MLP processing alone did not have a large effect on the overall performance , the best results were obtained for MLP-processed phase sensitive filters and added MFCCs , with relative error reductions of over 40 % for both noisy and clean training . Nevertheless , a model for predicting exchange rate fluctuations can enable business owners to make more appropriate judgments . This study employs a multilayer perceptions ( MLP ) neural network with genetic algorithm ( GA ) to predict the New Taiwan dollar (NTD)/U.S. dollar ( USD ) exchange rate . The GA is used to determine the optimum number of input and hidden nodes for a feedforward neural network , the optimum slope of the activation function , and the optimum learning rates and momentum coefficients . The results indicate that both the back-propagation MLP and the Bspline neurofuzzy models are comparable in terms of prediction accuracy , although the back-propagation MLP model is found to perform slightly better than the B-spline neurofuzzy model . In terms of model parsimony , the B-spline neurofuzzy model is found to be more parsimonious than the back-propagation MLP model . In terms of model transparency , the B-spline neurofuzzy model is found to provide a more explicit interpretation of the relationships between the model inputs and the corresponding outputs . In this paper , we introduce a novel spike-based learning rule for rate-coded deep SNNs , whereby the spike count of each neuron is used as a surrogate for gradient backpropagation . We evaluate the proposed learning rule by training deep spiking multi-layer perceptron ( MLP ) and spiking convolutional neural network ( CNN ) on the UCI machine learning and MNIST handwritten digit datasets . We show that the proposed learning rule achieves state-of-the-art accuracies on all benchmark datasets . Four different combination schemes are investigated . They are averaging , Karmarker 's linear programming algorithm , MLP combiner and fuzzy neural network . The performance is tested on four different real traffic data , MPEG video , JPEG video , Ethernet and Internet . Mel Frequency Cepstrum Coefficient ( MFCC ) was employed to extract the significant features from infant cry . The selected MFCC features were then used to train several ANN Multi-Layer Perceptron ( MLP ) . The simulation results showed each method is able to improve classifier performance . It is shown through experiments on text-dependent speaker verification that the new speaker model shows better performance and noise robustness compared with the conventional speaker model . Further experiment shows that using MLP merging and decision , the problem of variation of thresholds can be solved to some extent , and the robustness of the system can be improved effectively . For the past 2 years , Richards Bay Coal Terminal has been using high speed video imaging and neural techniques to automatically classify damages on critical conveyor belt which transport 60 million tons of coal per annum . A second project has involved the installation of 3 Cyclops Coastal Imaging Stations in Durban and Cape Town which use an RGB based MLP neural network to extract waterlines on a short timescale . This would otherwise involve expensive manual surveys and is presently being used by the Durban municipality to manage the dumping of dredged sand to maintain optimal beach widths . We concatenate all pooling vectors , i.e. , mean , max , and weighted pooling , into the fixed - length vector and then put the vector into the final multilayer perceptron ( MLP ) classifier . The MLP has one hidden layer with tanh activation and softmax output layer in our experiments . The entire model is trained end - to - end , through minimizing the cross - entropy loss . The word vectors are initialized with GloVe 300D pretrained vectors and fine - tuned during training . We apply dropout ( p = 0.5 ) on the output of the word embedding layer and the input and the output of the MLP layer . The size of mini - batches is set to 32 and Adadelta optimizer is used for optimization . GloVe pretrained word embeddings are used and remain fixed during training . The dimension of encoder states ( ) is set to 300 and a 1024D MLP with one or two hidden layers is used . We apply dropout to the word embeddings and the MLP layers . BaseModel . As introduced in section [ reference ] , BaseModel follows the Embedding & MLP architecture and is the base of most of subsequently developed deep networks for CTR modeling . It acts as a strong baseline for our model comparison . Therefore , the FastText is a special case of EXAM with an average pooling as the aggregation layer . In EXAM , we use a non - linear MLP to be the aggregation layer , and it will generalize FastText to a non - linear setting which might be more expressive than the original one . section : Experiments Thus , the proposed model concatenates all four of these and passes it as input to an multilayer perceptron ( MLP ) : A tag can then be predicted with a linear classifier that takes as input the output of the MLP enized / segmented . , applies a softmax function and chooses for each word the tag with highest probability . Using this approach , the number of parts stays small , enabling fast parsing . The labeling of an arc is performed using the same feature representation fed into a different MLP predictor : As before we use a margin based hinge loss . The labeler is trained on the gold trees . 5.1 and set d = 128 . On top of each level of the feature pyramid , we apply a small 5×5 MLP to predict 14×14 masks and object scores in a fully convolutional fashion , see Fig . 4 . Additionally , motivated by the use of 2 scales per octave in the image pyramid of [ reference ][ reference ] , we use a second MLP of input size 7×7 to handle half octaves . paragraph : Thresholding Supervised Results For both the multihead MLP and the single linear layer instantiating of , we found that thresholding predictions produced noticeably better results than using a fixed threshold value such as . This makes sense since the label classes for most categories are very imbalanced . Our deep learning procedures combine structural magnetic resonance imaging ( MRI ) , demographic , neuropsychological , and APOe4 genotyping data as input measures . The most novel characteristics of our machine learning model compared to previous ones are as follows : 1 ) multi-tasking , in the sense that our deep learning model jointly learns to simultaneously predict both MCI to AD conversion , and AD vs healthy classification which facilitates the relevant feature extraction for prognostication ; 2 ) the neural network classifier employs relatively few parameters compared to other deep learning architectures ( we use ~550,000 network parameters , orders of magnitude lower than other network designs ) without compromising network complexity and hence significantly limits data-overfitting ; 3 ) both structural MRI images and warp field characteristics , which quantify the amount of volumetric change compared to the common template , were used as separate input streams to extract as much information as possible from the MRI data . All the analyses were performed on a subset of the Alzheimer ’s Disease Neuroimaging Initiative ( ADNI ) database , for a total of n=785 participants ( 192 AD , 409 MCI , and184 healthy controls ( HC ) ) . Second , several texture features based on the histogram and co-occurrence matrix of grey levels were extracted . Finally , a neural network based classifier was trained with features extracted from 66 chest images to distinguish pneumoconiosis patients from normal cases . Another 29 images were used to assess the diagnosis performance of the classifier , giving an overall accuracy of 79.3 % . Neural network classifying method is used in this work to perform facial expression recognition . The processed expressions were the six most pertinent facial expressions and the neutral one . The third one is hybridmethod , where the training model is estimated using the facial data of allthe speakers and testing is performed on one of the speaker . The results of speaker dependent and hybrid methods show that theneural network classifier provides the best results . In the speaker dependentmethod , the accuracies of neural network classifier for speakerand speech/silence detection are 97.43 % and 98.73 % respectively . Based on the multi-spectrum samples of stationary meteorology satellite cloud images , the best distinguishing factors of cloud classification were distilled and a synthetical optimized cloud classifier combining the advantages of both self-organizing feature map ( SOFM ) and probabilistic neural network ( PNN ) was established by computing and analyzing the gray- gradient co-occurrence matrix and texture characters of satellite cloud image samples to overcome the shortcoming of single a neural network ( ANN ) classifier difficult to identify and classify complex cloud characters accurately and effectively . Firstly , the cloud samples were classified to identify and partition the analogical sample-sets by SOM without supervision , then the initial classification error was revised under the supervision and the initial classification results were optimized once more by using PNN.The experiments results showed that the synthetical SOM-PNN classifier can improve the distinguishing effect in that the total accuracy of cloud classification results could reach 92.4 % and the coefficient of Kappa was 90.82 , which excel other single-statistical classifier evidently . Supervised k nearest neighbour and unsupervised hierarchical agglomerative clustering algorithm can be enhanced through word mover ’s distance-based sentence distance metric to offer superior context-aware sentence categorisation performance . Advanced neural network-oriented classifier is able to achieve competing result on the benchmark streams via an aggregated recurrent unit incorporated with sophis- ticated convolving layer . The continually increasing number of textual snippets produced each year ne- cessitates ever improving information processing methods for searching , retrieving , and organising text . Our network is based on a state - of - the - art face verification network trained using the CASIA - WebFace dataset contaning million face images with identity labels . As a classification network , it employs the Softmax loss regularized with its proposed center loss . But it is difficult to directly fine - tune the network for pain intensity classification due to limited face images with pain labels . section : Domain - Adversarial Neural Networks ( DANN ) An original aspect of our approach is to explicitly implement the idea exhibited by Theorem 2 into a neural network classifier . That is , to learn a model that can generalize well from one domain to another , we ensure that the internal representation of the neural network contains no discriminative information about the origin of the input ( source or target ) , while preserving a low risk on the source ( labeled ) examples . In the following evaluation ( section [ reference ] ) , we present performance results of a model targeted for the Raspberry Pi 3 . First , a speaker - adaptive Gaussian Mixture Model Hidden Markov Model ( GMM - HMM ) is trained on the speech corpus to obtain a context - dependent bootstrapping model with which we align the full dataset and extract lattices to prepare the neural network training . We train a deep neural network , consisting of time - delay layers similar to those presented in , and long short - term memory layers similar to those of . ’s sample quality , raises the obvious question of whether or not G simply memorizes training points . To test this , we perform class - wise nearest neighbors analysis in pixel space and the feature space of pre - trained classifier networks ( Appendix [ reference ] ) . In addition , we present both interpolations between samples and class - wise interpolations ( where is held constant ) in Figures [ reference ] and [ reference ] . section : Localization Starting from our classification - trained network , we replace the classifier layers by a regression network and train it to predict object bounding boxes at each spatial location and scale . We then combine the regression predictions together , along with the classification results at each location , as we now describe . We construct and publicly release a dataset of 25 , 100 queries annotated with the probability of being a well - formed natural language question ( § [ reference ] ) . We then train a feed - forward neural network classifier that uses the lexical and syntactic features extracted from the query on this data ( § [ reference ] ) . On a test set of 3 , 850 queries , we report an accuracy of 70.1 % on the binary classification task . It is an online learning algorithm which builds useful incremental results , and is trivially parallelizable . These qualities make it suitable for a broad class of real world applications such as network classification , and anomaly detection . section : INTRODUCTION The task is to find a projection to maximize the between - group variance and minimize the within - group variance , where and are group mean and covariance . In MMD - GAN , the neural - network discriminator works in a similar way as LDA . By minimizing , the discriminator tackles two tasks : [ label=0 ) ] The POS tagging models explored were the Hidden Markov Model , Support Vector Machine , Conditional Random Fields , Long Short Term Memory ( LSTM ) Networks , Bidirectional LSTM Networks , and Logistic Regression . A Fully Connected Neural Network was used to build the ensemble model , and it achieved an accuracy of 94.02 % . Using smartphone cameras to capture skin induced color values , we create a low-cost alternative to expensive bilirubinometers . Through computer vision and deep learning , the final fully-connected feed-forward neural network achieves competitive results compared to Dräger JM-103 , a state-of-the-art bilirubinometer . In the critical cases where treatment is required , the neural network even outperforms Dräger JM-103 , resulting in 75 % fewer false negative predictions . The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features ( e.g. from AffectiveTweets Wekapackage ) . The proposed system ( EmoDet ) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements ( F1-Score 0.67 ) over the baseline model provided by Task 3 organizers ( F1score 0.58 ) . Using Twitter as an example of the social media platform , CAUTE learns a tweet-to-user encoder to infer the user features from tweet features and a user-to-tweet encoder to predict the tweet content from a combination of the user features and the tweet meta features . The residual errors of both encoders are then fed into a fully-connected neural network layer to detect whether a post was published by the specified user or by a hacker . Experimental results showed that the features learned by CAUTE are more informative than those generated by conventional representation learning methods . Neural networks with any kind of connections can always be sorted as cross-connected ones . According to traditional multi-layer feed-forward neural network , this paper elaborates the concept of completely-fully connected neural network and then puts forward a cross-connected multi-layer feed-forward neural network algorithm . By analyzing the error function of multi-layer feed-forward neural network , it can be theoretical proved that the cross-connected neural network can reach ideal results with more concise framework compared with the non-cross connected neural network . The goal of this thesis is to demonstrate practical advantages of supernets and how they can improve performance of standard training algorithms . A selection of supernet models iii are reviewed and compared with a standard fully connected neural network structure . The results of this practical evaluation show that there are definite benefits to the supernet technique , such as the Classification Based on Subnet Error ( CBSE ) model that works well with clusters that exhibit dissimilar local behavior . Actually , what we have to point out is , the explotation of hierarchical reinforment learning is not a must , but a good choice in sequence decision scenarios . We attempt to replace the hierarchical architecture by a fully connected layer . However , the model is so numerically sensitive that we can not operate a stable training on it the original training settings . And the vector is a vector summarization of and , which is used to model the STOP action probability . In summary , we use the sub - vectors to model and according to : where denotes inner product , is a fully - connected neural network with model parameter , denotes the element - wise sigmoid function , and is the softmax function with temperature parameter . Note that we use the inner product between the vectors and to compute the ( pre - softmax ) score for choosing the - th candidate action , where . In , hand - designed rules serve to identify primary and secondary objects of the questions . In the Neural Module Networks , the question is processed by a dependency parser , and fragments of the parse , selected with ad hoc fixed rules are associated with modules , are assembled into a full neural network . In contrast , our method is trained to make direct use of the output of a syntactic parser . [ b ] 0.31 [ b ] 0.31 [ b ] 0.31 Figure [ reference ] presents an example of uncertainty estimates from bootstrapped neural networks on a regression task with noisy data . We trained a fully - connected 2 - layer neural networks with 50 rectified linear units ( ReLU ) in each layer on 50 bootstrapped samples from the data . As is standard , we initialize these networks with random parameter values , this induces an important initial diversity in the models . Alternatively , small architecturally variant models are used for semantic image segmentation tasks . For example , a fully - connected convolutional neural network ( FCN ) also provides state - of - the - art results for image segmentation tasks in computer vision [ reference ] . We first want to verify that ELUs keep the mean activations closer to zero than other units . Fully connected deep neural networks with ELUs ( ) , ReLUs , and LReLUs ( ) were trained on the MNIST digit classification dataset while each hidden unit ’s activation was tracked . Each network had eight hidden layers of 128 units each , and was trained for 300 epochs by stochastic gradient descent with learning rate and mini - batches of size 64 . Note that the Inference Network only appears in the Egan - Ent - VI model . For experiments with the synthetic datasets , the following fully - connected feed forward neural networks are employed where FC and BN denote fully - connected layer and batch normalization layer respectively . Note that since the input noise to the generator has dimension 4 , the Inference Net output has dimension 4 * 2 , where the first 4 elements correspond the inferred mean , and the last 4 elements correspond to the inferred diagonal covariance matrix in log scale . - gram vectors as input . To experiment with fully - connected neural nets , as in CNN , we minimized square loss with regularization and optional dropout by SGD , and activation was fixed to rectifier . To generate bag - of - - gram vectors , on topic classification , we first set each component to where is the word frequency in the document and then scaled them to unit vectors , which we found always improved performance over raw frequency . In this paper , we present a memristor-based neural crossbar circuit to implement on-chip supervised learning rule . In our work , activation function of neuron is implemented with simple CMOS inverter to save area overhead . Importantly , we propose a compact learning cell with a crossbar latch consisting of two antiparallel oriented binary memristors . Suitable modifications of the stochastic leaky integrate-and-fire model are employed to describe the membrane potential of the input neurons while the processing neuron is modeled by means of a jump-diffusion process . A stochastic resonance behavior is detected for the processing neuron in correspondence with the " ghost " frequencies both in the harmonic and in the anharmonic case . The range of parameter values under which this behavior occurs is specified and an interpretation of the coincidence detection mechanism involved is provided . But data available are usually discrete and irregularly distributed . Based on the neuron network technique , methods of interpolation to the charted depth are suggested in this paper . Two algorithms based on Levenberg-Marquardt back-propaganda and radial-basis function networks are investigated respectively . Qualitative and quantitative analysis from these points of view are done for the double layers FHN neuron network model . Results indicate that , the stochastic resonance response of the double layers FHN neuron network is better than the single FHN neuron model , and has better stability , and can be effectively detected for input signal at a wider range of noise intensity . Correlations between interspike intervals are often seen in data from single neurons , and are due to a combination of intrinsic mechanisms and the temporal properties of the input stimulus . Here we review recent progress in our understanding of how intrinsic correlations arise in simple biophysically justified neuron models . We further describe the generic conditions under which these correlations enhance the rate of transfer of information about time-varying stimuli . The deep architecture with evolutionary fuzzy clustering followed by hybrid neural network , built upon novel neuron TROIKA , yields a powerful tool for face recognition applications . The proposed TROIKA neuron is based on nonlinear aggregation functions which enables our hybrid neural classifier to inculcate the benefits of its computational power resulting in superior learning and generalization . The proposed TROIKA neuron reduces the complexity of DHCM because very few neurons are sufficient to recognize a subject in database . This system is developed a model which is used to analyze the food material on the basis of behavior of the material . The proposed research designs a three layer neuron model using supervised learning technique . The result of this researche is used to study the food material and predict the different parameters of testing material . This algorithm conducts discrete Walsh conversion to the procedure input of network and the weight vector quantity changing with time , then trains the network by the data having been converted . In this algorithm , the aggregation computation of process neuron can be simplified and the complex integration procedure can be avoided , and the training of process neuron networks can equate to the training of common networks , namely the arbitrary function approximating problems are converted to the function optimization problems . The availability of this algorithm is proved by the simulation test . As large-scale , detailed network modeling projects are flourishing in the field of computational neuroscience , it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those . Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data . It is however difficult to compare these methods because of the lack of appropriate benchmark tests . After switching to transmitted light microscopy , neurons of interest are re-identified and visually approached in situ with patch-clamp pipettes . Here we introduce a simpler method for neuron targeting . With fluorophore-coated pipettes , fluorescently labeled neurons and the pipette tips are simultaneously imaged at the same fluorescence wavelength in the same microscope field , so that the neurons and even their neurites are targeted without suffering from chromatic aberration or mechanical complication in optics . All of the neurons in our networks utilize the max - with - zero nonlinearity . That is , their output is computed as where is the total input to the neuron ( equivalently , the output of the neuron ’s linear filter added to the bias ) . This nonlinearity has several advantages over traditional saturating neuron models , including a significant reduction in the training time required to reach a given error rate . This nonlinearity also reduces the need for contrast - normalization and similar data where nel . This operation ( usually including some output nonlinearity ) is referred to as a " neuron " . See Fig . Results : Table [ reference ] shows that using more than one output neuron in the discriminator significantly improved the performance of repulsive loss over the one - neuron case on CIFAR - 10 dataset . The reason may be that using insufficient output neurons makes it harder for the discriminator to learn an injective and discriminative representation of the data ( see Fig . This plot shows that we would not have been able to experiment with such large neural networks for this work if we had used traditional saturating neuron models . We are not the first to consider alternatives to traditional neuron models in CNNs . For example , Jarrett et al . In a nutshell , MTL is a novel learning method that helps deep neural nets converge faster while reducing the probability to overfit when using few labeled training data only . In particular , “ transfer ” means that DNN weights trained on large - scale data can be used in other tasks by two light - weight neuron operations : Scaling and Shifting ( SS ) , i.e. . “ This paper proposes the SVDNet for retrieval problems , with focus on the application of person re - identification ( re - ID ) . We view each weight vector within a fully connected ( FC ) layer in a convolutional neuron network ( CNN ) as a projection basis . It is observed that the weight vectors are usually highly correlated . We have applied the instances of the framework to a code corpus collected from GitHub containing thousands of Java and C++ programs implementing 50 different algorithms and data structures . Our evaluation results show that the use of Bi-NN indeed produces promising algorithm classification results both within one language and across languages , and the encoding of dependencies from code into the underlying neural networks helps improve algorithm classification accuracy further . In particular , our custom-built dependency trees with tree-based convolutional neural networks achieve the highest classification accuracy among the different instances of the framework that we have evaluated . Mel-frequency cepstral coefficients ( MFCC ) , delta Mel-frequency cepstral coefficients ( ΔMFCC ) , double delta Mel-frequency cepstral coefficients ( ΔΔMFCC ) based feature extraction techniques compute the acoustic feature and its statistical moments mean and correlation coefficient . Acoustic feature and its statistical moments passed through the different types of the algorithm-based classifier . By using different classifier find the efficiency of disguised voice . The purpose of this work is to create more defective specimen for development of deep learning model for non-destructive inspection of welding materials of the workpiece by radiographic testing . Inadequate amounts of defective samples in some rare cases poses a major challenge for the development of defect detection and classification deep learning models . Obtaining actual rare defects from real works is improbable in the industry . Person re - ID is different from image classification in that the training and testing sets contain entirely different classes . So a popular deep learning method for re - ID consists of 1 ) training a classification deep model on the training set , 2 ) extracting image descriptors using the fully - connected ( FC ) layer for the query and gallery images , and 3 ) computing similarities based on Euclidean distance before returning the sorted list . Our work is motivated by the observation that after training a convolutional neural network ( CNN ) for classification , the weight vectors within a fully - connected layer ( FC ) are usually highly correlated . DLDL consistently improves the network training process in our experiments , by preventing it from over - fitting when the training set is small . We empirically showed that DLDL produces robust and competitive performances than traditional classification or regression deep models on several popular visual recognition tasks . However , constructing a reasonable label distribution is still challenging due to the diversity of label space for different recognition tasks . We also discuss in - network upsampling ( Section [ reference ] ) , of which the fully connected prediction by Eigen et al . is a special case . Unlike these existing methods , we adapt and extend deep classification architectures , using image classification as supervised pre - training , and fine - tune fully convolutionally to learn simply and efficiently from whole image inputs and whole image ground thruths . Hariharan et al . and Gupta
ECG(Electrocardiogram ) , a field of Bio-signal , is generally experimented with classification algorithms most of which are SVM(Support Vector Machine ) , MLP(Multilayer Perceptron ) . But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm . The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed .
This new ANN architecture tackles the problem of context dependent behaviour training . Roughly , the principle is based on main MLP ( Multilayered Perceptron ) in which each synaptic weight connection value is estimated by another MLP ( an OWE ) with respect to context representation . In this paper , we present 2 hybrid systems for phoneme recognition .
For the Aurora2 noisy digit recognition task , the use of phase sensitive filters improved the MFCC baseline , whereas using filters that neglect phase information did not . While MLP processing alone did not have a large effect on the overall performance , the best results were obtained for MLP-processed phase sensitive filters and added MFCCs , with relative error reductions of over 40 % for both noisy and clean training .
Nevertheless , a model for predicting exchange rate fluctuations can enable business owners to make more appropriate judgments . This study employs a multilayer perceptions ( MLP ) neural network with genetic algorithm ( GA ) to predict the New Taiwan dollar (NTD)/U.S. dollar ( USD ) exchange rate . The GA is used to determine the optimum number of input and hidden nodes for a feedforward neural network , the optimum slope of the activation function , and the optimum learning rates and momentum coefficients .
The results indicate that both the back-propagation MLP and the Bspline neurofuzzy models are comparable in terms of prediction accuracy , although the back-propagation MLP model is found to perform slightly better than the B-spline neurofuzzy model . In terms of model parsimony , the B-spline neurofuzzy model is found to be more parsimonious than the back-propagation MLP model . In terms of model transparency , the B-spline neurofuzzy model is found to provide a more explicit interpretation of the relationships between the model inputs and the corresponding outputs .
In this paper , we introduce a novel spike-based learning rule for rate-coded deep SNNs , whereby the spike count of each neuron is used as a surrogate for gradient backpropagation . We evaluate the proposed learning rule by training deep spiking multi-layer perceptron ( MLP ) and spiking convolutional neural network ( CNN ) on the UCI machine learning and MNIST handwritten digit datasets . We show that the proposed learning rule achieves state-of-the-art accuracies on all benchmark datasets .
Four different combination schemes are investigated . They are averaging , Karmarker 's linear programming algorithm , MLP combiner and fuzzy neural network . The performance is tested on four different real traffic data , MPEG video , JPEG video , Ethernet and Internet .
Mel Frequency Cepstrum Coefficient ( MFCC ) was employed to extract the significant features from infant cry . The selected MFCC features were then used to train several ANN Multi-Layer Perceptron ( MLP ) . The simulation results showed each method is able to improve classifier performance .
It is shown through experiments on text-dependent speaker verification that the new speaker model shows better performance and noise robustness compared with the conventional speaker model . Further experiment shows that using MLP merging and decision , the problem of variation of thresholds can be solved to some extent , and the robustness of the system can be improved effectively .
For the past 2 years , Richards Bay Coal Terminal has been using high speed video imaging and neural techniques to automatically classify damages on critical conveyor belt which transport 60 million tons of coal per annum . A second project has involved the installation of 3 Cyclops Coastal Imaging Stations in Durban and Cape Town which use an RGB based MLP neural network to extract waterlines on a short timescale . This would otherwise involve expensive manual surveys and is presently being used by the Durban municipality to manage the dumping of dredged sand to maintain optimal beach widths .
We concatenate all pooling vectors , i.e. , mean , max , and weighted pooling , into the fixed - length vector and then put the vector into the final multilayer perceptron ( MLP ) classifier . The MLP has one hidden layer with tanh activation and softmax output layer in our experiments . The entire model is trained end - to - end , through minimizing the cross - entropy loss .
The word vectors are initialized with GloVe 300D pretrained vectors and fine - tuned during training . We apply dropout ( p = 0.5 ) on the output of the word embedding layer and the input and the output of the MLP layer . The size of mini - batches is set to 32 and Adadelta optimizer is used for optimization .
GloVe pretrained word embeddings are used and remain fixed during training . The dimension of encoder states ( ) is set to 300 and a 1024D MLP with one or two hidden layers is used . We apply dropout to the word embeddings and the MLP layers .
BaseModel . As introduced in section [ reference ] , BaseModel follows the Embedding & MLP architecture and is the base of most of subsequently developed deep networks for CTR modeling . It acts as a strong baseline for our model comparison .
Therefore , the FastText is a special case of EXAM with an average pooling as the aggregation layer . In EXAM , we use a non - linear MLP to be the aggregation layer , and it will generalize FastText to a non - linear setting which might be more expressive than the original one . section : Experiments
Thus , the proposed model concatenates all four of these and passes it as input to an multilayer perceptron ( MLP ) : A tag can then be predicted with a linear classifier that takes as input the output of the MLP enized / segmented . , applies a softmax function and chooses for each word the tag with highest probability .
Using this approach , the number of parts stays small , enabling fast parsing . The labeling of an arc is performed using the same feature representation fed into a different MLP predictor : As before we use a margin based hinge loss . The labeler is trained on the gold trees .
5.1 and set d = 128 . On top of each level of the feature pyramid , we apply a small 5×5 MLP to predict 14×14 masks and object scores in a fully convolutional fashion , see Fig . 4 . Additionally , motivated by the use of 2 scales per octave in the image pyramid of [ reference ][ reference ] , we use a second MLP of input size 7×7 to handle half octaves .
paragraph : Thresholding Supervised Results For both the multihead MLP and the single linear layer instantiating of , we found that thresholding predictions produced noticeably better results than using a fixed threshold value such as . This makes sense since the label classes for most categories are very imbalanced .
Our deep learning procedures combine structural magnetic resonance imaging ( MRI ) , demographic , neuropsychological , and APOe4 genotyping data as input measures . The most novel characteristics of our machine learning model compared to previous ones are as follows : 1 ) multi-tasking , in the sense that our deep learning model jointly learns to simultaneously predict both MCI to AD conversion , and AD vs healthy classification which facilitates the relevant feature extraction for prognostication ; 2 ) the neural network classifier employs relatively few parameters compared to other deep learning architectures ( we use ~550,000 network parameters , orders of magnitude lower than other network designs ) without compromising network complexity and hence significantly limits data-overfitting ; 3 ) both structural MRI images and warp field characteristics , which quantify the amount of volumetric change compared to the common template , were used as separate input streams to extract as much information as possible from the MRI data . All the analyses were performed on a subset of the Alzheimer ’s Disease Neuroimaging Initiative ( ADNI ) database , for a total of n=785 participants ( 192 AD , 409 MCI , and184 healthy controls ( HC ) ) .
Second , several texture features based on the histogram and co-occurrence matrix of grey levels were extracted . Finally , a neural network based classifier was trained with features extracted from 66 chest images to distinguish pneumoconiosis patients from normal cases . Another 29 images were used to assess the diagnosis performance of the classifier , giving an overall accuracy of 79.3 % .
Neural network classifying method is used in this work to perform facial expression recognition . The processed expressions were the six most pertinent facial expressions and the neutral one .
The third one is hybridmethod , where the training model is estimated using the facial data of allthe speakers and testing is performed on one of the speaker . The results of speaker dependent and hybrid methods show that theneural network classifier provides the best results . In the speaker dependentmethod , the accuracies of neural network classifier for speakerand speech/silence detection are 97.43 % and 98.73 % respectively .
Based on the multi-spectrum samples of stationary meteorology satellite cloud images , the best distinguishing factors of cloud classification were distilled and a synthetical optimized cloud classifier combining the advantages of both self-organizing feature map ( SOFM ) and probabilistic neural network ( PNN ) was established by computing and analyzing the gray- gradient co-occurrence matrix and texture characters of satellite cloud image samples to overcome the shortcoming of single a neural network ( ANN ) classifier difficult to identify and classify complex cloud characters accurately and effectively . Firstly , the cloud samples were classified to identify and partition the analogical sample-sets by SOM without supervision , then the initial classification error was revised under the supervision and the initial classification results were optimized once more by using PNN.The experiments results showed that the synthetical SOM-PNN classifier can improve the distinguishing effect in that the total accuracy of cloud classification results could reach 92.4 % and the coefficient of Kappa was 90.82 , which excel other single-statistical classifier evidently .
Supervised k nearest neighbour and unsupervised hierarchical agglomerative clustering algorithm can be enhanced through word mover ’s distance-based sentence distance metric to offer superior context-aware sentence categorisation performance . Advanced neural network-oriented classifier is able to achieve competing result on the benchmark streams via an aggregated recurrent unit incorporated with sophis- ticated convolving layer . The continually increasing number of textual snippets produced each year ne- cessitates ever improving information processing methods for searching , retrieving , and organising text .
Our network is based on a state - of - the - art face verification network trained using the CASIA - WebFace dataset contaning million face images with identity labels . As a classification network , it employs the Softmax loss regularized with its proposed center loss . But it is difficult to directly fine - tune the network for pain intensity classification due to limited face images with pain labels .
section : Domain - Adversarial Neural Networks ( DANN ) An original aspect of our approach is to explicitly implement the idea exhibited by Theorem 2 into a neural network classifier . That is , to learn a model that can generalize well from one domain to another , we ensure that the internal representation of the neural network contains no discriminative information about the origin of the input ( source or target ) , while preserving a low risk on the source ( labeled ) examples .
In the following evaluation ( section [ reference ] ) , we present performance results of a model targeted for the Raspberry Pi 3 . First , a speaker - adaptive Gaussian Mixture Model Hidden Markov Model ( GMM - HMM ) is trained on the speech corpus to obtain a context - dependent bootstrapping model with which we align the full dataset and extract lattices to prepare the neural network training . We train a deep neural network , consisting of time - delay layers similar to those presented in , and long short - term memory layers similar to those of .
’s sample quality , raises the obvious question of whether or not G simply memorizes training points . To test this , we perform class - wise nearest neighbors analysis in pixel space and the feature space of pre - trained classifier networks ( Appendix [ reference ] ) . In addition , we present both interpolations between samples and class - wise interpolations ( where is held constant ) in Figures [ reference ] and [ reference ] .
section : Localization Starting from our classification - trained network , we replace the classifier layers by a regression network and train it to predict object bounding boxes at each spatial location and scale . We then combine the regression predictions together , along with the classification results at each location , as we now describe .
We construct and publicly release a dataset of 25 , 100 queries annotated with the probability of being a well - formed natural language question ( § [ reference ] ) . We then train a feed - forward neural network classifier that uses the lexical and syntactic features extracted from the query on this data ( § [ reference ] ) . On a test set of 3 , 850 queries , we report an accuracy of 70.1 % on the binary classification task .
It is an online learning algorithm which builds useful incremental results , and is trivially parallelizable . These qualities make it suitable for a broad class of real world applications such as network classification , and anomaly detection . section : INTRODUCTION
The task is to find a projection to maximize the between - group variance and minimize the within - group variance , where and are group mean and covariance . In MMD - GAN , the neural - network discriminator works in a similar way as LDA . By minimizing , the discriminator tackles two tasks : [ label=0 ) ]
The POS tagging models explored were the Hidden Markov Model , Support Vector Machine , Conditional Random Fields , Long Short Term Memory ( LSTM ) Networks , Bidirectional LSTM Networks , and Logistic Regression . A Fully Connected Neural Network was used to build the ensemble model , and it achieved an accuracy of 94.02 % .
Using smartphone cameras to capture skin induced color values , we create a low-cost alternative to expensive bilirubinometers . Through computer vision and deep learning , the final fully-connected feed-forward neural network achieves competitive results compared to Dräger JM-103 , a state-of-the-art bilirubinometer . In the critical cases where treatment is required , the neural network even outperforms Dräger JM-103 , resulting in 75 % fewer false negative predictions .
The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features ( e.g. from AffectiveTweets Wekapackage ) . The proposed system ( EmoDet ) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements ( F1-Score 0.67 ) over the baseline model provided by Task 3 organizers ( F1score 0.58 ) .
Using Twitter as an example of the social media platform , CAUTE learns a tweet-to-user encoder to infer the user features from tweet features and a user-to-tweet encoder to predict the tweet content from a combination of the user features and the tweet meta features . The residual errors of both encoders are then fed into a fully-connected neural network layer to detect whether a post was published by the specified user or by a hacker . Experimental results showed that the features learned by CAUTE are more informative than those generated by conventional representation learning methods .
Neural networks with any kind of connections can always be sorted as cross-connected ones . According to traditional multi-layer feed-forward neural network , this paper elaborates the concept of completely-fully connected neural network and then puts forward a cross-connected multi-layer feed-forward neural network algorithm . By analyzing the error function of multi-layer feed-forward neural network , it can be theoretical proved that the cross-connected neural network can reach ideal results with more concise framework compared with the non-cross connected neural network .
The goal of this thesis is to demonstrate practical advantages of supernets and how they can improve performance of standard training algorithms . A selection of supernet models iii are reviewed and compared with a standard fully connected neural network structure . The results of this practical evaluation show that there are definite benefits to the supernet technique , such as the Classification Based on Subnet Error ( CBSE ) model that works well with clusters that exhibit dissimilar local behavior .
Actually , what we have to point out is , the explotation of hierarchical reinforment learning is not a must , but a good choice in sequence decision scenarios . We attempt to replace the hierarchical architecture by a fully connected layer . However , the model is so numerically sensitive that we can not operate a stable training on it the original training settings .
And the vector is a vector summarization of and , which is used to model the STOP action probability . In summary , we use the sub - vectors to model and according to : where denotes inner product , is a fully - connected neural network with model parameter , denotes the element - wise sigmoid function , and is the softmax function with temperature parameter . Note that we use the inner product between the vectors and to compute the ( pre - softmax ) score for choosing the - th candidate action , where .
In , hand - designed rules serve to identify primary and secondary objects of the questions . In the Neural Module Networks , the question is processed by a dependency parser , and fragments of the parse , selected with ad hoc fixed rules are associated with modules , are assembled into a full neural network . In contrast , our method is trained to make direct use of the output of a syntactic parser .
[ b ] 0.31 [ b ] 0.31 [ b ] 0.31 Figure [ reference ] presents an example of uncertainty estimates from bootstrapped neural networks on a regression task with noisy data . We trained a fully - connected 2 - layer neural networks with 50 rectified linear units ( ReLU ) in each layer on 50 bootstrapped samples from the data . As is standard , we initialize these networks with random parameter values , this induces an important initial diversity in the models .
Alternatively , small architecturally variant models are used for semantic image segmentation tasks . For example , a fully - connected convolutional neural network ( FCN ) also provides state - of - the - art results for image segmentation tasks in computer vision [ reference ] .
We first want to verify that ELUs keep the mean activations closer to zero than other units . Fully connected deep neural networks with ELUs ( ) , ReLUs , and LReLUs ( ) were trained on the MNIST digit classification dataset while each hidden unit ’s activation was tracked . Each network had eight hidden layers of 128 units each , and was trained for 300 epochs by stochastic gradient descent with learning rate and mini - batches of size 64 .
Note that the Inference Network only appears in the Egan - Ent - VI model . For experiments with the synthetic datasets , the following fully - connected feed forward neural networks are employed where FC and BN denote fully - connected layer and batch normalization layer respectively . Note that since the input noise to the generator has dimension 4 , the Inference Net output has dimension 4 * 2 , where the first 4 elements correspond the inferred mean , and the last 4 elements correspond to the inferred diagonal covariance matrix in log scale .
- gram vectors as input . To experiment with fully - connected neural nets , as in CNN , we minimized square loss with regularization and optional dropout by SGD , and activation was fixed to rectifier . To generate bag - of - - gram vectors , on topic classification , we first set each component to where is the word frequency in the document and then scaled them to unit vectors , which we found always improved performance over raw frequency .
In this paper , we present a memristor-based neural crossbar circuit to implement on-chip supervised learning rule . In our work , activation function of neuron is implemented with simple CMOS inverter to save area overhead . Importantly , we propose a compact learning cell with a crossbar latch consisting of two antiparallel oriented binary memristors .
Suitable modifications of the stochastic leaky integrate-and-fire model are employed to describe the membrane potential of the input neurons while the processing neuron is modeled by means of a jump-diffusion process . A stochastic resonance behavior is detected for the processing neuron in correspondence with the " ghost " frequencies both in the harmonic and in the anharmonic case . The range of parameter values under which this behavior occurs is specified and an interpretation of the coincidence detection mechanism involved is provided .
But data available are usually discrete and irregularly distributed . Based on the neuron network technique , methods of interpolation to the charted depth are suggested in this paper . Two algorithms based on Levenberg-Marquardt back-propaganda and radial-basis function networks are investigated respectively .
Qualitative and quantitative analysis from these points of view are done for the double layers FHN neuron network model . Results indicate that , the stochastic resonance response of the double layers FHN neuron network is better than the single FHN neuron model , and has better stability , and can be effectively detected for input signal at a wider range of noise intensity .
Correlations between interspike intervals are often seen in data from single neurons , and are due to a combination of intrinsic mechanisms and the temporal properties of the input stimulus . Here we review recent progress in our understanding of how intrinsic correlations arise in simple biophysically justified neuron models . We further describe the generic conditions under which these correlations enhance the rate of transfer of information about time-varying stimuli .
The deep architecture with evolutionary fuzzy clustering followed by hybrid neural network , built upon novel neuron TROIKA , yields a powerful tool for face recognition applications . The proposed TROIKA neuron is based on nonlinear aggregation functions which enables our hybrid neural classifier to inculcate the benefits of its computational power resulting in superior learning and generalization . The proposed TROIKA neuron reduces the complexity of DHCM because very few neurons are sufficient to recognize a subject in database .
This system is developed a model which is used to analyze the food material on the basis of behavior of the material . The proposed research designs a three layer neuron model using supervised learning technique . The result of this researche is used to study the food material and predict the different parameters of testing material .
This algorithm conducts discrete Walsh conversion to the procedure input of network and the weight vector quantity changing with time , then trains the network by the data having been converted . In this algorithm , the aggregation computation of process neuron can be simplified and the complex integration procedure can be avoided , and the training of process neuron networks can equate to the training of common networks , namely the arbitrary function approximating problems are converted to the function optimization problems . The availability of this algorithm is proved by the simulation test .
As large-scale , detailed network modeling projects are flourishing in the field of computational neuroscience , it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those . Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data . It is however difficult to compare these methods because of the lack of appropriate benchmark tests .
After switching to transmitted light microscopy , neurons of interest are re-identified and visually approached in situ with patch-clamp pipettes . Here we introduce a simpler method for neuron targeting . With fluorophore-coated pipettes , fluorescently labeled neurons and the pipette tips are simultaneously imaged at the same fluorescence wavelength in the same microscope field , so that the neurons and even their neurites are targeted without suffering from chromatic aberration or mechanical complication in optics .
All of the neurons in our networks utilize the max - with - zero nonlinearity . That is , their output is computed as where is the total input to the neuron ( equivalently , the output of the neuron ’s linear filter added to the bias ) . This nonlinearity has several advantages over traditional saturating neuron models , including a significant reduction in the training time required to reach a given error rate . This nonlinearity also reduces the need for contrast - normalization and similar data
where nel . This operation ( usually including some output nonlinearity ) is referred to as a " neuron " . See Fig .
Results : Table [ reference ] shows that using more than one output neuron in the discriminator significantly improved the performance of repulsive loss over the one - neuron case on CIFAR - 10 dataset . The reason may be that using insufficient output neurons makes it harder for the discriminator to learn an injective and discriminative representation of the data ( see Fig .
This plot shows that we would not have been able to experiment with such large neural networks for this work if we had used traditional saturating neuron models . We are not the first to consider alternatives to traditional neuron models in CNNs . For example , Jarrett et al .
In a nutshell , MTL is a novel learning method that helps deep neural nets converge faster while reducing the probability to overfit when using few labeled training data only . In particular , “ transfer ” means that DNN weights trained on large - scale data can be used in other tasks by two light - weight neuron operations : Scaling and Shifting ( SS ) , i.e. . “
This paper proposes the SVDNet for retrieval problems , with focus on the application of person re - identification ( re - ID ) . We view each weight vector within a fully connected ( FC ) layer in a convolutional neuron network ( CNN ) as a projection basis . It is observed that the weight vectors are usually highly correlated .
We have applied the instances of the framework to a code corpus collected from GitHub containing thousands of Java and C++ programs implementing 50 different algorithms and data structures . Our evaluation results show that the use of Bi-NN indeed produces promising algorithm classification results both within one language and across languages , and the encoding of dependencies from code into the underlying neural networks helps improve algorithm classification accuracy further . In particular , our custom-built dependency trees with tree-based convolutional neural networks achieve the highest classification accuracy among the different instances of the framework that we have evaluated .
Mel-frequency cepstral coefficients ( MFCC ) , delta Mel-frequency cepstral coefficients ( ΔMFCC ) , double delta Mel-frequency cepstral coefficients ( ΔΔMFCC ) based feature extraction techniques compute the acoustic feature and its statistical moments mean and correlation coefficient . Acoustic feature and its statistical moments passed through the different types of the algorithm-based classifier . By using different classifier find the efficiency of disguised voice .
The purpose of this work is to create more defective specimen for development of deep learning model for non-destructive inspection of welding materials of the workpiece by radiographic testing . Inadequate amounts of defective samples in some rare cases poses a major challenge for the development of defect detection and classification deep learning models . Obtaining actual rare defects from real works is improbable in the industry .
Person re - ID is different from image classification in that the training and testing sets contain entirely different classes . So a popular deep learning method for re - ID consists of 1 ) training a classification deep model on the training set , 2 ) extracting image descriptors using the fully - connected ( FC ) layer for the query and gallery images , and 3 ) computing similarities based on Euclidean distance before returning the sorted list . Our work is motivated by the observation that after training a convolutional neural network ( CNN ) for classification , the weight vectors within a fully - connected layer ( FC ) are usually highly correlated .
DLDL consistently improves the network training process in our experiments , by preventing it from over - fitting when the training set is small . We empirically showed that DLDL produces robust and competitive performances than traditional classification or regression deep models on several popular visual recognition tasks . However , constructing a reasonable label distribution is still challenging due to the diversity of label space for different recognition tasks .
We also discuss in - network upsampling ( Section [ reference ] ) , of which the fully connected prediction by Eigen et al . is a special case . Unlike these existing methods , we adapt and extend deep classification architectures , using image classification as supervised pre - training , and fine - tune fully convolutionally to learn simply and efficiently from whole image inputs and whole image ground thruths . Hariharan et al . and Gupta
Research on the modified algorithm for improving accuracy of Random Forest classifier which identifies automatically arrhythmia Continuous speech recognition using a context sensitive ANN and HMM2s Comparing Different Flavors of Spectro-Temporal Features for ASR Using a Multilayer Perceptrons Neural Network and Genetic Algorithm to Predict the NTD/USD Exchange Rate Neural and neurofuzzy techniques applied to modelling settlement of shallow foundations on granular soils Deep Spiking Neural Network with Spike Count based Learning Rule Multi-scale network traffic prediction using k-factor Gegenbauer ARMA and MLP models Sequential process of Mel Frequency Cepstrum Coefficient (MFCC) and Binary Particle Swarm Optimization (BPSO) technique for improving the performance of Multi-Layer Perceptron (MLP) to detect asphyxia diseases through infant cries / Azlee Zabidi Research on The Noise Robustness of Speaker Verification System Based on Sub-band HMM/MLP Model Application of neural networks to solve industrial problems: bridging in practice Neural Natural Language Inference Models Enhanced with External Knowledge Learning to Compose Task-Specific Tree Structures Cell-aware Stacked LSTMs for Modeling Sentences Deep Interest Network for Click-Through Rate Prediction Explicit Interaction Model towards Text Classification Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Feature Pyramid Networks for Object Detection Practical Text Classification With Large Pre-Trained Language Models A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease Computer-Aided Diagnosis for Pneumoconiosis Based on Texture Analysis on Digital Chest Radiographs Face expression recognition using AR-BURG model and neural network Towards Speaker Detectionusing FaceAPI Facial Movementsin Human-Machine Multiparty Dialogue A Cloud Classification Model of Multi-spectrum Satellite Cloud Images Based on the Network Coupling SOFM with PNN Context-aware sentence categorisation: word mover's distance and character-level convolutional recurrent neural network Transferring Face Verification Nets To Pain and Expression Regression Domain-Adversarial Training of Neural Networks Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Large Scale GAN Training for High Fidelity Natural Image Synthesis OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Identifying Well-formed Natural Language Questions DeepWalk: online learning of social representations Improving MMD-GAN Training with Repulsive Loss Function Automated Error Correction and Validation for POS Tagging of Hindi A machine learning approach for jaundice detection using color corrected smartphone images EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning You have been CAUTE! Early Detection of Compromised Accounts on Social Media Research on structure of multi-layer feed-forward neural network with cross connections Neural Supernets: Structuring Artificial Neural Networks According to Cluster Analysis Long Text Generation via Adversarial Training with Leaked Information M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Graph-Structured Representations for Visual Question Answering Deep Exploration via Bootstrapped DQN Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Calibrating Energy-based Generative Adversarial Networks Effective Use of Word Order for Text Categorization with Convolutional Neural Networks On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron Ghost Stochastic Resonance for a Neuron with a Pair of Periodic Inputs Charted Depth Interpolation: Neuron Network Approaches Research on neuron network of weak signal based on stochastic resonance detection ISI CORRELATIONS AND INFORMATION TRANSFER On the Deep Hybrid Computational Model for Face Recognition Neuron Model To Analyze The Infection Behavior In Sample Food Material Learning algorithm of process neural networks based on discrete Walsh conversion The quantitative single-neuron modeling competition Fluorescent pipettes for optically targeted patch-clamp recordings Improving neural networks by preventing co-adaptation of feature detectors Aggregated Residual Transformations for Deep Neural Networks Improving MMD-GAN Training with Repulsive Loss Function ImageNet classification with deep convolutional neural networks Meta-Transfer Learning for Few-Shot Learning SVDNet for Pedestrian Retrieval Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification Multimedia analysis for disguised voice and classification efficiency Creating Subsurface Defect Specimens for Deep Learning Analyzing of Radiographic Weld Testing SVDNet for Pedestrian Retrieval Deep Label Distribution Learning With Label Ambiguity Fully Convolutional Networks for Semantic Segmentation
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EUROSPEECH INTERSPEECH AICCSA ACL AAAI ACML KDD AAAI ACL Transactions of the Association for Computational Linguistics 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ArXiv J. Mach. Learn. Res. ArXiv ICLR ICLR EMNLP KDD '14 ICLR PACLIC SemEval@NAACL-HLT ASONAM AAAI NeurIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS ArXiv ICLR ICLR HLT-NAACL NANOARCH BVAI ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR CACM 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) SANER 2017 IEEE International Conference on Computer Vision (ICCV) IEEE Transactions on Image Processing IEEE Transactions on Pattern Analysis and Machine Intelligence
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