102 citations · 570 across the 31 of their papers we have counts for
8 papers · 1 filter
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
Yu-Ying Liu, Alexander Moreno, Maxwell A. Xu +7
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in…
Generative Particle Variational Inference via Estimation of Functional Gradients
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference…
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform
Jun Li, Li Fuxin, Sinisa Todorovic
Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, w…
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Efficient exploration remains a challenging problem in reinforcement learning, especially for those tasks where rewards from environments are sparse. A commonly used approach for e…
Counterfactual States for Atari Agents via Generative Deep Learning
Matthew L. Olson, Lawrence Neal, Fuxin Li +1
Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past…
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff, Li Fuxin
Standard neural networks are often overconfident when presented with data outside the training distribution. We introduce HyperGAN, a new generative model for learning a distributi…
Interactive Naming for Explaining Deep Neural Networks: A Formative Study
Mandana Hamidi-Haines, Zhongang Qi, Alan Fern +2
We consider the problem of explaining the decisions of deep neural networks for image recognition in terms of human-recognizable visual concepts. In particular, given a test set of…
A Linear Approximation to the chi^2 Kernel with Geometric Convergence
Fuxin Li, Guy Lebanon, Cristian Sminchisescu
We propose a new analytical approximation to the kernel that converges geometrically. The analytical approximation is derived with elementary methods and adapts to the input…