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Found 7 papers, 3 papers with code
Federated Reinforcement Learning with Environment Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction.
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods.
Positive Seebeck coefficient in highly doped La$_{2-x}$Sr$_x$CuO$_4$ ($x$=0.33); its origin and implication
We present a study of the thermoelectric (Seebeck and Nernst) response in heavily overdoped, non-superconducting La$_{1. 67}$Sr$_{0. 33}$CuO$_4$.
Superconductivity Materials Science Strongly Correlated Electrons
Natural Language Adversarial Defense through Synonym Encoding
In the area of natural language processing, deep learning models are recently known to be vulnerable to various types of adversarial perturbations, but relatively few works are done on the defense side.
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks
Stochastic variance-reduced gradient (SVRG) is a classical optimization method.
Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent
Specifically, we impose a regularization term on the learning rate via a generalized distance, and cast the joint updating process of the parameter and the learning rate into a maxmin problem.
Practical Verifiable In-network Filtering for DDoS defense
In this paper, we show the technical feasibility of verifiable in-network filtering, called VIF, that offers filtering verifiability to DDoS victims and neighbor ASes.
Cryptography and Security
