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Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals Tom Dupré La T our
In this paper, we propose to learn dedicated representations of such recordings using a multivariate convolu-tional sparse coding (CSC) algorithm.
Geomagnetic superstorm shrunk Earth's protective plasmasphere
In 2024, superstorm Gannon generated auroras-and wreaked havoc on this radiation-blocking layer. Breakthroughs, discoveries, and DIY tips sent every weekday. Last year, the most violent geomagnetic storm to strike Earth in over two decades did more than disrupt GPS systems and internet connections. According to a study published today in the journal, superstorm Gannon also squeezed the planet's protective layer of ionized particles to one-fifth its normal size. Geomagnetic storms aren't rare occurrences, but most of them remain relatively benign.
MetaReg: Towards Domain Generalization using Meta-Regularization
Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa
Existing machine learning algorithms including deep neural networks achieve good performance in cases where the training and the test data are sampled from the same distribution. While this is a reasonable assumption to make, it might not hold true in practice.
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Action Elimination Network (AEN) that eliminates sub-optimal actions.
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny
The complementarity between SGD and EA is worth investigating.
Neural Proximal Gradient Descent for Compressive Imaging
Morteza Mardani, Qingyun Sun, David Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John Pauly
Neural Information Processing Systems http://nips.cc/
Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.
Distributed Stochastic Optimization via Adaptive SGD
Ashok Cutkosky, Róbert Busa-Fekete
Stochastic convex optimization algorithms are the most popular way to train machine learning models on large-scale data.
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
We prove that the proposed algorithm converges to the optimal solution at a global geometric rate.