9 citations · 15 across the 8 of their papers we have counts for
9 papers
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
Kuilin Chen, Chi-Guhn Lee
Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pret…
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
Raj Patel, Chia-Wei Hsing, Serkan Sahin +11
Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in…
Meta-free few-shot learning via representation learning with weight averaging
Kuilin Chen, Chi-Guhn Lee
Recent studies on few-shot classification using transfer learning pose challenges to the effectiveness and efficiency of episodic meta-learning algorithms. Transfer learning approa…
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Michael Gimelfarb, André Barreto, Scott Sanner +1
Sample efficiency and risk-awareness are central to the development of practical reinforcement learning (RL) for complex decision-making. The former can be addressed by transfer le…
Attentive Gaussian processes for probabilistic time-series generation
Kuilin Chen, Chi-Guhn Lee
The transduction of sequence has been mostly done by recurrent networks, which are computationally demanding and often underestimate uncertainty severely. We propose a computationa…
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Resolving the exploration-exploitation trade-off remains a fundamental problem in the design and implementation of reinforcement learning (RL) algorithms. In this paper, we focus o…
Bayesian Experience Reuse for Learning from Multiple Demonstrators
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Learning from demonstrations (LfD) improves the exploration efficiency of a learning agent by incorporating demonstrations from experts. However, demonstration data can often come…
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. Ho…
The Bayesian process control with multiple assignable causes
Jue Wang, Chi-Guhn Lee
We study an optimal process control problem with multiple assignable causes. The process is initially in-control but is subject to random transition to one of multiple out-of-contr…