Technology
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang
Neural Information Processing Systems http://nips.cc/
Compact Generalized Non-local Network
Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu
Neural Information Processing Systems http://nips.cc/
Visualizing the PHATE of Neural Networks
Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne
Wedemonstrate that our visualization provides intuitive, detailed summaries of the learning dynamics beyond simple global measures (i.e., validation loss and accuracy), without the need to access validation data. Furthermore, M-PHATE better captures both the dynamics and community structure of the hidden units as compared to visualization based on standard dimensionality reduction methods (e.g., ISOMAP,t-SNE).
A Causal Framework for Decomposing Spurious Variations
Often, however, the causal relationship may be entirely absent or account only for a part of the initially observed correlation.
Don't take it lightly: Phasing optical random projections with unknown operators
Sidharth Gupta, Remi Gribonval, Laurent Daudet, Ivan Dokmanić
In this paper we tackle the problem of recovering the phase of complex linear measurements whenonlymagnitude information isavailableandwecontrol the input. We are motivated by the recent development of dedicated optics-based hardware for rapid random projections which leverages the propagation of light inrandom media.