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7 papers · 1 filter
Occam's razor is insufficient to infer the preferences of irrational agents
Stuart Armstrong, Sören Mindermann
Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from observed behavior. Since human planning systematically deviates from rationality, several a…
Noisy Natural Gradient as Variational Inference
Guodong Zhang, Shengyang Sun, David Duvenaud +1
Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff between cheap but simple variatio…
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
Will Grathwohl, Dami Choi, Yuhuai Wu +2
Gradient-based optimization is the foundation of deep learning and reinforcement learning. Even when the mechanism being optimized is unknown or not differentiable, optimization us…
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
Ding Liu, Shi-Ju Ran, Peter Wittek +4
The resemblance between the methods used in quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learni…
Insightful classification of crystal structures using deep learning
A. Ziletti, D. Kumar, M. Scheffler +1
Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a cru…
Towards Diverse and Natural Image Descriptions via a Conditional GAN
Bo Dai, Sanja Fidler, Raquel Urtasun +1
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect.Sentences produced by existing methods, e.g. those based on RNNs,…