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Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum
Neural Information Processing Systems http://nips.cc/
PCA of high dimensional random walks with comparison to neural network training
Joseph Antognini, Jascha Sohl-Dickstein
Neural Information Processing Systems http://nips.cc/
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su
The regularizer P λi|b|(i) is a sorted `1-norm (denoted asJλ(b) henceforth), which isnon-separabledue to the sorting operation involved in its calculation.
Better Exploration with Optimistic Actor Critic
Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann
Actor-critic methods, a type of model-free Reinforcement Learning, have beensuccessfully applied to challenging tasks in continuous control, often achievingstate-of-the artperformance.
Iterative Value-Aware Model Learning
Neural Information Processing Systems http://nips.cc/
GroundedAnswersforMulti-agentDecision-making ProblemthroughGenerativeWorldModel
Theempirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark.
Natasha 2: Faster Non-Convex Optimization Than SGD
In diverse world of deep learning research has given rise to numerous architectures for neural networks(convolutionalones,longshorttermmemoryones,etc). However,tothisdate,theunderlying training algorithms for neural networks are still stochastic gradient descent (SGD) and its heuristic variants.
Scalable Deep Generative Relational Model with High-Order Node Dependence
Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
Neural Information Processing Systems http://nips.cc/