Deep Learning
Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira
Neural Information Processing Systems http://nips.cc/
Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R. M. Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations.
The streaming rollout of deep networks - towards fully model-parallel execution
Volker Fischer, Jan Koehler, Thomas Pfeil
The streaming rollout maximizes these properties and enables a fully parallel execution of the network reducing runtime on massively parallel devices.
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel
In recent years, deep generative models have been shown to'imagine' convincing
Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog
Sang-Woo Lee, Yu-Jung Heo, Byoung-Tak Zhang
To ask the adequate question, deep learning and reinforcement learning have been recently applied.
Adversarial Text Generation via Feature-Mover's Distance
Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin
Generative adversarial networks (GANs) have achieved significant success in generating real-valued data.
A General Method for Amortizing Variational Filtering
Joseph Marino, Milan Cvitkovic, Yisong Yue
The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step.
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
The purpose of this work is to study the general properties of this quantity and to show how they can be leveraged in practice.
Learning sparse neural networks via sensitivity-driven regularization
Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini
The complexity of a neural network can be reduced by promoting sparse interconnection structures.
Critical initialisation for deep signal propagation in noisy rectifier neural networks
Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper
Neural Information Processing Systems http://nips.cc/