Deep Learning Publication Navigator - subtopic: stochastic

Year TitleAuthor
2017   Deep Stochastic Radar Models  TA Wheeler, M Holder, H Winner, M Kochenderfer
2017   Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks  J Li, Z Yuan, Z Li, C Ding, A Ren, Q Qiu, J Draper
2017   Constructing a Deep Regression Model Utilizing Cascaded Sparse Autoencoders and Stochastic Gradient Descent  A Moussavi
2017   Doubly Stochastic Adversarial Autoencoder  M Azarafrooz
2017   Doubly Stochastic Variational Inference for Deep Gaussian Processes  H Salimbeni, M Deisenroth
2017   Stochastic Replica Voting Machine prediction of stable Perovskite and binary alloys  B Sun, T Mazaheri, J Scher
2017   A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks  Q Su, X Liao, L Carin
2017   Optimizing Expectations: From Deep Reinforcement Learning To Stochastic Computation Graphs  P Abbeel
2017   Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC  Y Cong, B Chen, H Liu, M Zhou
2017   Unifying the Stochastic Spectral Descent for Restricted Boltzmann Machines with Bernoulli or Gaussian Inputs  K Fan
2017   Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations  J Han, A Jentzen
2017   Stochastic reconstruction of an oolitic limestone by generative adversarial networks  L Mosser, O Dubrule, MJ Blunt 
2017   Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation  L Hasenclever, S Webb, T Lienart, S Vollmer
2017   Stochastic Modeling And Uncertainty Evaluation For Performance Prognosis In Dynamical Systems  P Wang
2017   Stochastic Decorrelation Constraint Regularized Auto-Encoder for Visual Recognition  F Mao, W Xiong, B Du, L Zhang
2017   Normalization and dropout for stochastic computing-based deep convolutional neural networks  J Li, Z Yuan, Z Li, A Ren, C Ding, J Draper, S Nazarian 
2017   Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks  D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic 
2017   Learning Approximate Stochastic Transition Models  Y Song, C Grimm, X Wang, ML Littman 
2017   Stochastic Computation Of Gammatone Filter Based Hearing Aid For Impaired People  R Priyanka, M Ranjani, R Vidhya, S Vigneshwari
2017   Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution  PW Chou, D Maturana, S Scherer
2017   Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling  S Lai, L Jin, W Yang
2017   Stochastic Configuration Networks: Fundamentals and Algorithms  D Wang, M Li
2017   Simplified Stochastic Feedforward Neural Networks  K Lee, J Kim, S Chong, J Shin
2017   Area/Energy-Efficient Gammatone Filters Based on Stochastic Computation  N Onizawa, S Koshita, S Sakamoto, M Abe
2017   Deep Stochastic Configuration Networks: Universal Approximation and Learning Representation  D Wang, M Li
2017   Motion Prediction Under Multimodality with Conditional Stochastic Networks  K Fragkiadaki, J Huang, A Alemi, S Vijayanarasimhan
2017   Acceleration Of Convolutional Neural Network Training Using Stochastic Perforation  L Chang, S Gupta
2017   Evaluation of Stochastic Cascaded IIR Filters  N Onizawa, S Koshita, S Sakamoto, M Kawamata
2017   Learning Deep Generative Models With Doubly Stochastic Gradient MCMC  C Du, J Zhu, B Zhang
2017   A New Stochastic Computing Multiplier with Application to Deep Convolutional Neural Networks  H Sim, J Lee
2016   SEBOOST-Boosting Stochastic Learning Using Subspace Optimization Techniques  E Richardson, R Herskovitz, B Ginsburg, M Zibulevsky
2016   Stochastic Neural Networks with Monotonic Activation Functions  S Ravanbakhsh, B Poczos, J Schneider
2016   An Optimized Second Order Stochastic Learning Algorithm for Neural Network Training  SS Liew, M Khalil
2016   SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing  A Ren, J Li, Z Li, C Ding, X Qian, Q Qiu, B Yuan
2016   Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server  YW Teh, L Hasenclever, T Lienart, S Vollmer, S Webb
2016   Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning  S Zheng, Q Meng, T Wang, W Chen, N Yu, ZM Ma
2016   Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features  A Subramaniam, V Patel, A Mishra
2016   Stochastic Variational Deep Kernel Learning  AG Wilson, Z Hu, R Salakhutdinov, EP Xing
2016   Stochastic neuromorphic learning machines for weakly labeled data  E Neftci
2016   Simple Evolutionary Optimization Can Rival Stochastic Gradient Descent in Neural Networks  G Morse, KO Stanley
2016   Orthogonal Echo State Networks and stochastic evaluations of likelihoods  NM Mayer
2016   Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent  L Wang, Y Yang, MR Min, S Chakradhar
2016   Distributed stochastic optimization for deep learning  S Zhang
2016   Distributed stochastic optimization for deep learning (thesis)  S Zhang
2016   Simulation of Bayesian Learning and Inference on Distributed Stochastic Spiking Neural Networks  K Ahmed, A Shrestha, Q Qiu
2016   Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles  S Lee, S Purushwalkam, M Cogswell, V Ranjan
2016   Guided Linear Dimensionality Reduction by Stochastic Gradient Descent  M Richter
2016   Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning  A Nguyen, J Yosinski, J Clune
2016   Joint Stochastic Approximation learning of Helmholtz Machines  H Xu, Z Ou
2016   Distributed Deep Learning Using Synchronous Stochastic Gradient Descent  D Das, S Avancha, D Mudigere, K Vaidynathan
2016   Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing  A Ren, Z Li, Y Wang, Q Qiu, B Yuan
2016   Functional Systems Network Outperforms Q-learning in Stochastic Environment  AY Sorokin, MS Burtsev
2016   Deep Networks with Stochastic Depth  G Huang, Y Sun, Z Liu, D Sedra, K Weinberger
2016   Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks  K Kim, J Kim, J Yu, J Seo, J Lee, K Choi
2015   Innovation engines: Automated creativity and improved stochastic optimization via deep learning  A Nguyen, J Yosinski, J Clune
2015   FPGA implementation of a Deep Belief Network architecture for character recognition using stochastic computation  K Sanni, G Garreau, JL Molin, AG Andreou
2015   Systems And Methods For Combining Stochastic Average Gradient And Hessian-Free Optimization For Sequence Training Of Deep …  P Dognin, V Goel
2015   Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking  TH Wen, M Gašic, D Kim, N Mrkšic, PH Su, D Vandyke
2015   High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models  C Li, C Chen, K Fan, L Carin
2015   Analysis and Synthesis of Stochastic Nonlinear Systems  S Zhong, J Cheng, J Cao, P Balasubramaniam, H Du
2015   Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM  A Punjani, MA Brubaker
2015   Evaluating unsupervised fault detection in self-healing systems using stochastic primitives  C Schneider, AD Barker, SA Dobson
2015   Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks  C Li, C Chen, D Carlson, L Carin
2015   Fast Second-Order Stochastic Backpropagation for Variational Inference  K Fan, Z Wang, J Beck, J Kwok, K Heller
2015   On Graduated Optimization for Stochastic Non-Convex Problems  E Hazan, KY Levy, S Shalev
2015   A-SDLM: An Asynchronous Stochastic Learning Algorithm For Fast Distributed Learning  M Khalil
2015   Learning Deep Generative Models with Doubly Stochastic MCMC  C Du, J Zhu, B Zhang
2015   Stochastic Interpretation of Quasi-periodic Event-based Systems  H Mostafa, G Indiveri
2015   " Oddball SGD": Novelty Driven Stochastic Gradient Descent for Training Deep Neural Networks  AJR Simpson
2015   Perturbed Iterate Analysis for Asynchronous Stochastic Optimization  H Mania, X Pan, D Papailiopoulos, B Recht
2015   Large Scale Optimization with Proximal Stochastic Newton-Type Gradient Descent  Z Shi, R Liu
2015   Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization  X Lian, Y Huang, Y Li, J Liu
2015   Accelerating Stochastic Gradient Descent via Online Learning to Sample  G Bouchard, T Trouillon, J Perez, A Gaidon
2015   Stochastic Spectral Descent for Restricted Boltzmann Machines  D Carlson, V Cevher, L Carin
2015   Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation  TD Bui, JM Hernández
2015   Stochastic gradient method with accelerated stochastic dynamics  M Ohzeki
2015   GSNs: Generative Stochastic Networks  G Alain, Y Bengio, L Yao, J Yosinski
2015   Beyond Convexity: Stochastic Quasi-Convex Optimization  E Hazan, KY Levy, S Shalev
2015   FPGA emulation of a spike-based, stochastic system for real-time image dewarping  JL Molin, T Figliolia, K Sanni, I Doxas, A Andreou
2015   VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing  A Ardakani, F Leduc
2015   Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods  B Poole, S EDU
2014   ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient  C Gulcehre, Y Bengio
2014   Investigation of stochastic Hessian-Free optimization in Deep neural networks for speech recognition  Z You, B Xu
2014   Improving training time of deep neural networkwith asynchronous averaged stochastic gradient descent  Z You, B Xu
2014   General Stochastic Networks for Classification  M Zöhrer, F Pernkopf
2014   Stochastic Descent Analysis of Representation Learning Algorithms  RM Golden
2014   A Simple Stochastic Algorithm for Structural Features Learning  J Macák, O Drbohlav