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

Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of network units.

In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution.

We present a learning-based approach to computing solutions for certain NPhard problems. Our approach combines deep learning techniques with useful algorithmic elements from classic heuristics. The central component is a graph convolutional network that is trained to estimate the likelihood, for each vertex in a graph, of whether this vertex is part of the optimal solution.

To mitigate the threat of adversarial examples, a large number of methods have been developed.

To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions.

Actively selecting camera views for "undoing" occlusions and recovering missing information has

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

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