Deep Learning
Flexible and accurate inference and learning for deep generative models
Helmholtz machine and later variational autoencoder algorithms (but unlike adversarial methods) our approach learns an explicit inference or "recognition" model
Knowledge Distillation by On-the-Fly Native Ensemble
xu lan, Xiatian Zhu, Shaogang Gong
Neural Information Processing Systems http://nips.cc/
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi, Cao Xiao, Walter Stewart, Jimeng Sun
Work done at Georgia Institute of Technology. Work done at Sutter Health.
Neural Architecture Optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu
Automatic neural architecture design has shown its potential in discovering powerful neural network architectures.
Implicit Reparameterization Gradients
Mikhail Figurnov, Shakir Mohamed, Andriy Mnih
However, it is not applicable to a number of important continuous distributions.
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
Yuanxiang Gao, Li Chen, Baochun Li
Training deep neural networks requires an exorbitant amount of computation resources, including a heterogeneous mix of GPU and CPU devices. It is critical to place operations in a neural network on these devices in an optimal way, so that the training process can complete within the shortest amount of time. The state-of-the-art uses reinforcement learning to learn placement skills by repeatedly performing Monte-Carlo experiments. However, due to its equal treatment of placement samples, we argue that there remains ample room for significant improvements.
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
Boyuan Pan, Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He
Machine comprehension (MC) has gained significant popularity over the past few years and it is a coveted goal in the field of natural language understanding.
Compact Generalized Non-local Network
Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu
Neural Information Processing Systems http://nips.cc/
Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar
Equal Contribution 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. is often attributed to the rapid decay in the learning rate when gradients are dense, which is often the case in many machine learning applications.
PAC-learning in the presence of adversaries
Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
Neural Information Processing Systems http://nips.cc/