Deep Learning
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO
Neural Information Processing Systems http://nips.cc/
Hyperbolic Graph Convolutional Neural Networks
Hyperbolic geometry offers an exciting alternative, as it enables embeddings with much smaller distortion.
important time points and observations throughout the time series
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.
Adapting Neural Architectures Between Domains Y anxi Li1, Zhaohui Y ang 2,3, Yunhe Wang
Neural architecture search (NAS) has demonstrated impressive performance in automatically designing high-performance neural networks. The power of deep neural networks is to be unleashed for analyzing a large volume of data (e.g.
03573b32b2746e6e8ca98b9123f2249b-AuthorFeedback.pdf
AUTHOR RESPONSE TO THE REVIEWS OF "CORMORANT: COVARIANT MOLECULAR NEURAL NETWORKS" We thank all three reviewers for their insightful comments and positive evaluations of our manuscript. We will update the paper to reflect their suggestions by the camera ready deadline. We will shortly release a Python library that implements the Cormorant architecture. Currently we are just cleaning up and documenting the code. As for the other points brought up by the reviewers we have the following comments: 1. Structure and supplement: As suggested by Reviewer 2, we will move some details of the technical implementation in Section 4.4 to the supplement.
DTWNet: a Dynamic Time Warping Network
Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran
Neural Information Processing Systems http://nips.cc/