46 citations · 91 across the 5 of their papers we have counts for
6 papers
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Xiangyu Kong, Bo Xin, Fangchen Liu +1
Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take…
From Bayesian Sparsity to Gated Recurrent Nets
Hao He, Bo Xin, David Wipf
The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights.…
Collaborative Deep Reinforcement Learning for Joint Object Search
Xiangyu Kong, Bo Xin, Yizhou Wang +1
We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under intera…
Maximal Sparsity with Deep Networks?
Bo Xin, Yizhou Wang, Wen Gao +1
The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural n…
Background Subtraction via Generalized Fused Lasso Foreground Modeling
Bo Xin, Yuan Tian, Yizhou Wang +1
Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, du…
Stable Feature Selection from Brain sMRI
Bo Xin, Lingjing Hu, Yizhou Wang +1
Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are app…