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[PDF] Top 20 Mean-field Langevin System, Optimal Control and Deep Neural Networks

Has 10000 "Mean-field Langevin System, Optimal Control and Deep Neural Networks" found on our website. Below are the top 20 most common "Mean-field Langevin System, Optimal Control and Deep Neural Networks".

Mean-field Langevin System, Optimal Control and Deep Neural Networks

Mean-field Langevin System, Optimal Control and Deep Neural Networks

... the deep neural ...layer) neural networks using the mean-field Langevin ...continuous-time optimal control problem as a model to study the deep ... Voir le document complet

25

Game on Random Environement, Mean-field Langevin System and Neural Networks

Game on Random Environement, Mean-field Langevin System and Neural Networks

... MFL system under quite mild conditions on the ...(deep) neural networks can be viewed as a minimization problem (or optimal control problem in the context of deep ... Voir le document complet

25

Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks

Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks

... of deep neural ...multi-layers neural networks. Deep neural networks trained with stochastic gradient descent algorithm proved to be extremely successful in number of ... Voir le document complet

29

Ergodicity of the underdamped mean-field Langevin dynamics

Ergodicity of the underdamped mean-field Langevin dynamics

... the neural networks can be viewed as a numerical discretization scheme for the overdamped MFL ...Similar mean-field analysis has been done to deep networks, optimal ... Voir le document complet

29

Approximating optimal state feedback using neural networks

Approximating optimal state feedback using neural networks

... Figure 7: Data in the Center of Arcs Contributes Little to Training Symptoms of these trajectory based training examples were found both close to the origin, where [r] ... Voir le document complet

24

Dynamic Programming for Mean-Field Type Control

Dynamic Programming for Mean-Field Type Control

... the extension of the HJB dynamic programming approach. The second difficulty is related to the well-posedness of the HJB or adjoint equation because it is set in an infinite domain in space. The third difficulty is the ... Voir le document complet

21

Unsupervised post-tuning of deep neural networks

Unsupervised post-tuning of deep neural networks

... embedding and its parameter vector. These r normalized neural layers implement equation ...model and number of parameters are the same between CVDD and our approach: only the loss ... Voir le document complet

9

Multichannel Music Separation with Deep Neural Networks

Multichannel Music Separation with Deep Neural Networks

... DNNs, and the spatial covariance matrices, which are updated iteratively in an EM ...voice and other instruments from a mixture containing multiple musical ...evaluation, and estimating the ... Voir le document complet

6

Dynamic Programming for Mean-field type Control

Dynamic Programming for Mean-field type Control

... noticed earlier in [9] and in [4] 1 , there seems to be no such restriction if one works with the probability measure of X t and use the Fokker-Planck equation. In this note we apply the dynamic programming ... Voir le document complet

10

Comparing learned representations of deep neural networks

Comparing learned representations of deep neural networks

... images, and have very high adversarial example ...representations and whether it is a direct result of the architecture design, particularly the grouped convolution ... Voir le document complet

64

Probabilistic Robustness Estimates for Deep Neural Networks

Probabilistic Robustness Estimates for Deep Neural Networks

... 2004) and CALIFORNIA (Pace & Barry, 1997) datasets. The neural network and its training and testing are implemented in the python (Team, 2015) environment using the keras (Chollet et ... Voir le document complet

10

Learning Activation Functions in Deep Neural Networks

Learning Activation Functions in Deep Neural Networks

... the field of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...studies and practical applications of deep ... Voir le document complet

171

Deep neural networks for audio scene recognition

Deep neural networks for audio scene recognition

... The CASR problem consists in determining automatically the context or environment around a device [1]. A variety of features have been proposed for CASR, but the majority of the past work uses features that are ... Voir le document complet

6

Attacking the brain with neuroscience : mean-field theory, finite size effects and encoding capability of stochastic neural networks

Attacking the brain with neuroscience : mean-field theory, finite size effects and encoding capability of stochastic neural networks

... brain, and therefore an extension was ...chemical and heat properties of these materials, operating by analogy with the system that represents the problem to ...equations, and then the EAC is ... Voir le document complet

300

Deep neural networks for natural language processing and its acceleration

Deep neural networks for natural language processing and its acceleration

... From Figure 2, we report the performance of our model when trained with ground- truth trees as input. It is encouraging to see that our recurrent-recursive encoder improves performance over Transformer (FAN) [159] ... Voir le document complet

140

Deep neural networks are lazy : on the inductive bias of deep learning

Deep neural networks are lazy : on the inductive bias of deep learning

... 4.3 and 4.4.2 that the networks are just "lazy": they thrive to learn simple things first, before memorizing more complicated ...data and prioritizing learning on low-dimensional data ... Voir le document complet

78

Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

... feed-forward neural networks (FFNNs) such as CNNs and real-valued RNNs have shown to excel in a wide variety of applications and learning ...recurrent neural networks are ... Voir le document complet

146

Applications of complex numbers to deep neural networks

Applications of complex numbers to deep neural networks

... networks on toy ...matrices and they applied their method on toy tasks and on a real-world speech ...in neural networks also has biological motivations. Reichert and Serre [22] ... Voir le document complet

57

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

... not deterministic. Thus, to obtain the values reported in Table 1 , we repeated the prediction over the training data 20 times for the empirical loss computation of the selected epoch. The inference repetition process ... Voir le document complet

21

Singing voice detection with deep recurrent neural networks

Singing voice detection with deep recurrent neural networks

... MLP, and in time. RNNs are inherently deep in time, since their hidden vectors are a function of all the pre- vious ...ineffective and the network fails to learn long-term ... Voir le document complet

6

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