Deep Learning
Neural Similarity Learning
Weiyang Liu, Zhen Liu, James M. Rehg, Le Song
Neural Information Processing Systems http://nips.cc/
Geometry-Aware Neural Rendering
Joshua Tobin, Wojciech Zaremba, Pieter Abbeel
Agents can learn 3D structure implicitly (e.g., using end-to-end reinforcement learning [
Variational Graph Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian
In this paper, we propose a novel node embedding method for dynamic graphs that maps each node to a random vector in the latent space.
Region Mutual Information Loss for Semantic Segmentation
Shuai Zhao, Yang Wang, Zheng Yang, Deng Cai
Neural Information Processing Systems http://nips.cc/
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical model. However, in many cases the generating process is only a poor approximation of the much more complex true data generating process, leading to suboptimal estimations.
Tensor Monte Carlo: Particle Methods for the GPU era
Neural Information Processing Systems http://nips.cc/
Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation
It is well-known in the video understanding community that human action recognition models suffer from background bias, i.e., over-relying on scene cues in making