Deep Learning
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
Neural Information Processing Systems http://nips.cc/
On the Number of Linear Regions of Deep Neural Networks
Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/
Model-based Adversarial Meta-Reinforcement Learning
Meta-reinforcement learning and multi-task reinforcement learning aim to improve the sample efficiency by leveraging the shared structure within a family of tasks.
Multi-Resolution Weak Supervision for Sequential Data
Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Rรฉ
Neural Information Processing Systems http://nips.cc/
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao, Kaisheng Ma
Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in both storage and computation, which has restricted the deployment of deep neural networks on resource-limited edge devices. To address this problem, we propose the so-called SCAN framework for networks training and inference, which is orthogonal and complementary to existing acceleration and compression methods.
Biological Credit Assignment through Dynamic Inversion of Feedforward Networks William F. Podlaski
Learning depends on changes in synaptic connections deep inside the brain.
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
Training GCNs for a large graph, however, is still a challenge.