Deep Learning
Variational Memory Addressing in Generative Models
Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende
To illustrate the advantages of this approach we incorporate it into a variational autoencoder and apply the resulting model to the task of generative few-shot learning. The intuition behind this architecture is that the memory module can pick a relevant template from memory and the continuous part of the model can concentrate on modeling remaining variations.
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions.
Pose Guided Person Image Generation
Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool
Generating realistic-looking images is of great value for many applications such as face editing, movie making and image retrieval based on synthesized images.
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang
Classical results state that depth-bounded (e.g.
Dilated Recurrent Neural Networks
Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A. Hasegawa-Johnson, Thomas S. Huang
Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task.
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex M. Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
GibbsNet is the best of both worlds both in theory and in practice.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
Neural Information Processing Systems http://nips.cc/
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
Self-paced learning and hard example mining re-weight training instances to improve learning accuracy.
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz
Neural Information Processing Systems http://nips.cc/