Deep Learning
Convex Deep Learning via Normalized Kernels
Özlem Aslan, Xinhua Zhang, Dale Schuurmans
Neural Information Processing Systems http://nips.cc/
Knowledge Extraction with No Observable Data
Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang
Neural Information Processing Systems http://nips.cc/
Focused Quantization for Sparse CNNs
Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu
Neural Information Processing Systems http://nips.cc/
What Can ResNet Learn Efficiently, Going Beyond Kernels?
How can neural networks such as ResNet efficiently learn CIFAR-10 with test accuracy more than 96%, while other methods, especially kernel methods, fall relatively behind? Can we more provide theoretical justifications for this gap? Recently, there is an influential line of work relating neural networks to kernels in the over-parameterized regime, proving they can learn certain concept class that is also learnable by kernels with similar test error. Y et, can neural networks provably learn some concept class better than kernels? We answer this positively in the distribution-free setting.
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
Applying probabilistic models to reinforcement learning (RL) enables the uses of powerful optimisation tools such as variational inference in RL.
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently.
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel
Neural Information Processing Systems http://nips.cc/
