Year  Title  Author 
2017

Constrained Lowrank Matrix Estimation: Phase Transitions, Approximate Message Passing and Applications 
T Lesieur, F Krzakala, L Zdeborová

2017

Wildfire: Approximate synchronization of parameters in distributed deep learning 
R Nair, S Gupta

2017

Learning Approximate Stochastic Transition Models 
Y Song, C Grimm, X Wang, ML Littman

2017

ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks 
X Xu, S Das, K Kreutz

2017

ResilienceAware Frequency Tuning for NeuralNetworkBased 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 Kroneckerfactored 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 Kroneckerfactored Approximate Curvature 
J Martens, R Grosse
