[PDF] Top 20 Auto-Encoders, Distributed Training and Information Representation in Deep Neural Networks
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Auto-Encoders, Distributed Training and Information Representation in Deep Neural Networks
... Stochastic Networks We introduce a novel training principle for generative probabilistic models that is an alternative to maximum ...Denoising Auto-Encoders (DAE) and is based on ... Voir le document complet
191
Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks
... Phase information encoding for each of the activation functions tested for the Deep Complex ...part and the y-axis axis represents the imaginary part ; The figure on the right corresponds to the case ... Voir le document complet
146
Adaptive structured noise injection for shallow and deep neural networks
... of deep neural networks (DNN) for many machine learning tasks such as image classification and object recognition (Krizhevsky et ...relevant information in the data at different ... Voir le document complet
17
Multimodal and Crossmodal Representation Learning from Textual and Visual Features with Bidirectional Deep Neural Networks for Video Hyperlinking
... embedding and crossmodal mapping, as well as different dif- ferent single-modal representations to jointly embed descrip- tors in a new multimedia representation for the task of video ...modalities: ... Voir le document complet
9
Deep neural networks for audio scene recognition
... DNN In this paragraph, we consider a fixed number of 15 input frames (so the size of the input layer of our DNN is 15× 30 = 450) and we compare the performances in function of the number of hidden ... Voir le document complet
6
Probabilistic Robustness Estimates for Deep Neural Networks
... network and acts also as an overall control on the contraction power of the network by coupling lay- ers and allowing some weights to grow for some layers as long as in other layers others weights ... Voir le document complet
10
Comparing learned representations of deep neural networks
... Engineering in Electrical Engineering and Computer Science Abstract In recent years, a variety of deep neural network architectures have obtained substan- tial accuracy improvements ... Voir le document complet
64
Unsupervised post-tuning of deep neural networks
... classes in subsets, or bags, of ob- served samples [25]. This situation occurs in many important applications, for instance to train a neural network on quantum physics experiments, where assigning a ... Voir le document complet
9
Multichannel Music Separation with Deep Neural Networks
... ONCLUSION In this article, we presented a DNN-based multichannel source separation framework where the multichannel filter is derived using the source spectra, which are estimated by DNNs, and the spatial ... Voir le document complet
6
DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents
... datasets). In contrast, we outline that cosine values for our clustering approach seem to be more intuitive, with an average cosine for the GOV2 dataset higher than ...documents and lower than ...difference ... Voir le document complet
11
Unsupervised Layer-Wise Model Selection in Deep Neural Networks
... Abstract. Deep Neural Networks (DNN) propose a new and ef- ficient ML architecture based on the layer-wise building of several representation ...neurons in each DNN ... Voir le document complet
7
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
... ms), and human behav- ioral testing (S2 ...the neural representational space changes with increased viewing time and multiple ...engaged in passive viewing and human subjects were ... Voir le document complet
19
Clinical event prediction and understanding with deep neural networks
... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ... Voir le document complet
56
De-noising and de-blurring of images using deep neural networks
... In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, att[r] ... Voir le document complet
12
Mean-field Langevin System, Optimal Control and Deep Neural Networks
... measures and the regularizer Ent is the relative entropy with respect to the Lebesgue measure, see ...analyzed in the recent paper [16], this result is basically due to the fact that the function ν 7→ R F ... Voir le document complet
25
Deep neural networks for natural language processing and its acceleration
... embeddings in a sentence ...LSTM and then max-pooling over all of its hidden ...sentence information by making the model sensitive to the ordering of the ...words, and then learns to ... Voir le document complet
140
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
... Usually in deep learning framework training loops, the empirical loss of an epoch is computed as the averaged loss of each ...complete training data at the end of each ...used in our ... Voir le document complet
21
Coarse-graining auto-encoders for molecular dynamics
... Coarse-graining auto-encoding framework. a The model consists of an encoder and decoder, and is trained by reconstructing the original all-atom data by encoding atomistic trajectories through a ... Voir le document complet
10
X-Ray Sobolev Variational Auto-Encoders
... cost and the latent cost are required for good generative ...”SW” in the following) and the Cramer-Wold distances [ 23 ] (named ...distances. In fact, evidence is already present in the ... Voir le document complet
26
Feedback Control for Online Training of Neural Networks
... Online Training of Neural Networks Zilong Zhao 1 , Sophie Cerf 1 , Bogdan Robu 1 and Nicolas Marchand 1 Abstract— Convolutional neural networks (CNNs) are com- monly used for ... Voir le document complet
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