Deep Learning Publication Navigator - subtopic: approximate


Year TitleAuthor
2017   Constrained Low-rank Matrix Estimation: Phase Transitions, Approximate Message Passing and Applications  T Lesieur, F Krzakala, L Zdeborová
2017   ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks  X Xu, S Das, K Kreutz
2017   Resilience-Aware Frequency Tuning for Neural-Network-Based Approximate Computing Chips  Y Wang, J Deng, Y Fang, H Li, X Li
2016   Iterative Refinement Of Approximate Posteriors For Training Directed Belief Networks  RD Hjelm, K Cho, J Chung, R Salakhutdinov
2016   A Kronecker-factored approximate Fisher matrix for convolution layers  R Grosse, J Martens
2016   Iterative Refinement of the Approximate Posterior for Directed Belief Networks  D Hjelm, RR Salakhutdinov, K Cho, N Jojic, V Calhoun
2016   A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding  R Zhang, J Wu, C Zhang, WT Freeman, JB Tenenbaum
2016   Approximate computing: Challenges and opportunities  A Agrawal, J Choi, K Gopalakrishnan, S Gupta, R Nair
2016   Approximate Projection Expression of Nonlinear Artificial Intelligent Systems Based on Matrix Operator Polynomial Approximations  K Yuichi, K Takuro
2016   Deep Gaussian Processes for Regression using Approximate Expectation Propagation  TD Bui, D Hernández
2015   Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network  B Jiang, T Wu, C Zheng, WH Wong
2015   Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference  Y Gal, Z Ghahramani
2015   Approximate computing and the quest for computing efficiency  S Venkataramani, ST Chakradhar, K Roy
2015   Optimizing Neural Networks with Kronecker-factored Approximate Curvature  J Martens, R Grosse