Deep Learning
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Zelun Luo, Yuliang Zou, Judy Hoffman, Li F. Fei-Fei
Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe
Neural Information Processing Systems http://nips.cc/
Collaborative Deep Learning in Fixed Topology Networks
Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar
CDMSGD) algorithm for collaborative deep learning over fixed topology networks that enables data parallelization as well as decentralized computation.
Scalable Model Selection for Belief Networks
Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin
The factorized asymptotic Bayesian ( FAB) approach has recently been shown as a scalable model-selection framework for latent variable models.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO
Precipitation nowcasting refers to the problem of providing very short range (e.g., 0-6 hours) forecast
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, Daniel Soudry
We examine the initial high learning rate training phase.
Runtime Neural Pruning
Ji Lin, Yongming Rao, Jiwen Lu, Jie Zhou
The agent judges the importance of each convolutional kernel and conducts channel-wise pruning conditioned on different samples, where the network is pruned more when the image is easier for the task.
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Urs Köster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William Constable, Oguz Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao
Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem.