[PDF] Top 20 Deep neural networks are lazy : on the inductive bias of deep learning
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Deep neural networks are lazy : on the inductive bias of deep learning
... of the prior are the network architecture, the number of parameters, the non-linear activation functions, and the initialization ...for Deep Learning ... Voir le document complet
78
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
... Very deep neural networks demonstrate to have a high perfor- mance on many visual-related ...they are more difficult to ...make the network training pro- cess faster while attaining ... Voir le document complet
7
On the Inductive Bias of Neural Tangent Kernels
... studied the inductive bias of the “lazy training” regime for over-parameterized neural networks, by considering the neural tangent kernel of ... Voir le document complet
25
Deep neural networks for choice analysis
... by the challenges in the application of DNN to choice analysis, including the tension between domain-specific knowledge and generic-purpose mod- els, and the lack of ... Voir le document complet
128
On Recurrent and Deep Neural Networks
... we are actually minimizing the cross-entropy during training ...(i.e. the gradients are computed based on the cross-entropy cost, square error is only computed for ...visualization). ... Voir le document complet
267
Deep Learning for Image Memorability Prediction : the Emotional Bias
... Convolutional Neural Network The GoogleNet architecture is a concatenation of nine similar “Inception” ...reduce the resolution. Given the depth of the network, two ... Voir le document complet
6
Extensive deep neural networks for transferring small scale learning to large scale systems
... implementation are why EDNNs are so useful; the physical property that permits the decomposition of the problem is actually revealed by the data ...that the data ... Voir le document complet
14
Predicting the Propagation of Acoustic Waves using Deep Convolutional Neural Networks
... prediction of noise generated by aero-acoustic sources has been approached in the last 50 years with a large range of numerical and analytical ...limiting the approach to academic and ... Voir le document complet
19
Deep neural networks for direct, featureless learning through observation: the case of two-dimensional spin models
... in the training ...(with the ex- ception of the Potts model) can be represented as binary-valued arrays, with each element having a value of either σ = −1 (spin-down) or σ = 1 ...amount ... Voir le document complet
11
Learning Sparse deep neural networks using efficient structured projections on convex constraints for green AI
... OTIVATION Deep neural networks have been applied recently to different domains and have shown a dramatic improvement in accuracy of image recognition [1], speech recognition [2] or natural ... Voir le document complet
9
Reinforcement learning, energy systems and deep neural nets
... Reinforcement learning for trading in the intraday market More: “Intra-day Bidding Strategies for Storage Devices Using Deep ...Proceedings of the 2018 15th International Conference on ... Voir le document complet
18
Compression of Deep Neural Networks for Image Instance Retrieval
... is the problem of retrieving images from a database which contain the same ...Convolutional Neural Network (CNN) based descriptors are becoming the dominant approach for ... Voir le document complet
11
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
Deep Convolutional Networks are Hierarchical Kernel Machines
... class of learning algorithms called deep learning, and in particular convolutional net- works, are based on a basic operation in multiple ...using the notation of ... Voir le document complet
17
Probabilistic Robustness Estimates for Deep Neural Networks
... In the case of deep dense neural networks, un- der random noise attacks, we propose to study the probability that the output of the network de- viates from ... Voir le document complet
10
Multichannel Music Separation with Deep Neural Networks
... where the multichannel filter is derived using the source spectra, which are estimated by DNNs, and the spatial covariance matrices, which are updated iteratively in an EM ...in ... Voir le document complet
6
Impact of reverberation through deep neural networks on adversarial perturbations
... 2.1. The reverberation procedure as proposed in cognitive psychology In contrast to human brain, ANNs tend to forget their previous knowledge when learning new information, a well- known problem called ... Voir le document complet
10
Deep neural networks for automatic classification of anesthetic-induced unconsciousness
... Hospital of Liège, Liège, Belgium Abstract. Despite the common use of anesthetics to modulate consciousness in the clinic, brain-based monitoring of consciousness is ...measurement ... Voir le document complet
11
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms
... [7]. The only difference between DQV and DQV-Max, and DQN and DDQN is the exploration schedule which is ...from the latter two algorithms, which use an epsilon-greedy strategy which has an starting ... Voir le document complet
8
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... embed the one-hot input into a dense word representation. It en- codes both the syntactic and semantic meaning of the ...words. The semantically relevant words can be found by ... Voir le document complet
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