output
20022025
most citedNILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

500 citations

Showing 2019Show all

25 papers · 1 filter

cs.LG20194 cited

Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

Mark Edmonds, Xiaojian Ma, Siyuan Qi +3

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning chall…

cs.LG20199 cited

Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance

Mingxuan Jing, Xiaojian Ma, Wenbing Huang +4

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations…

eess.IV20199 cited

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

Yantao Lu, Yunhan Jia, Jianyu Wang +4

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other m…

cs.LG20191 cited

Learning Robust Representations with Graph Denoising Policy Network

Lu Wang, Wenchao Yu, Wei Wang +5

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing p…

astro-ph.CO2019

The Fundamentals of the 21-cm Line

Steven R. Furlanetto

We review some of the fundamental physics necessary for computing the highly-redshifted spin-flip background. We first discuss the radiative transfer of the 21-cm line and define t…

astro-ph.CO2019

Physical Cosmology From the 21-cm Line

Steven R. Furlanetto

We describe how the high-redshift 21-cm background can be used to improve both our understanding of the fundamental cosmological parameters of our Universe and exotic processes ori…