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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.
Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar
The first prominent algorithms in this line of research isADAGRAD [7,22], which uses a per-dimension learning rate based on squared pastgradients.ADAGRADachievedsignificant performance gainsincomparison toSGDwhenthe gradientsaresparse.
Part I Appendix Table of Contents
Table 9) and identifying reflections (Error #20 in Table 13) are also noted.
9023effe3c16b0477df9b93e26d57e2c-Paper.pdf
This simple intuition makes Bayesian methods appealing approaches for transfer in RL, and many previous works have been proposed in this direction.
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
Neural Information Processing Systems http://nips.cc/
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily Fox, Christopher Nemeth
Neural Information Processing Systems http://nips.cc/
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman, Francis Bach, Sebastien Bubeck, Laurent Massoulié, Yin Tat Lee
Neural Information Processing Systems http://nips.cc/