Neural Information Processing Systems http://nips.cc/

One approach might be to capture the important structure of the current environment in a maximally compact way (to preserve capacity for future learning). Such learning is likely to result in positive transfer if future training domains share some structural similarity with the old ones.

Existing methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results.

Deep neural networks are known to benefit from normalization between consecutive layers.

One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have many bad local minima.

Boltzmann machines (BMs) are appealing candidates for powerful priors in varia-tional autoencoders (V AEs), as they are capable of capturing nontrivial and multi-modal distributions over discrete variables.

We present an end-to-end framework for solving the V ehicle Routing Problem (VRP) using reinforcement learning.

Neural Information Processing Systems http://nips.cc/

We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.

Neural Information Processing Systems http://nips.cc/