87 citations · 300 across the 16 of their papers we have counts for
6 papers · 1 filter
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
Zelin Zhao, Chuang Gan, Jiajun Wu +2
Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers ro…
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
Duo Wang, Yu Cheng, Mo Yu +2
Few-shot learning aims to learn classifiers for new classes with only a few training examples per class. Most existing few-shot learning approaches belong to either metric-based me…
Hybrid Reinforcement Learning with Expert State Sequences
Xiaoxiao Guo, Shiyu Chang, Mo Yu +2
Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a mo…
Few-shot Learning with Meta Metric Learners
Yu Cheng, Mo Yu, Xiaoxiao Guo +1
Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approache…
Eigenoption Discovery through the Deep Successor Representation
Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo +3
Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learn…
Robust Task Clustering for Deep Many-Task Learning
Mo Yu, Xiaoxiao Guo, Jinfeng Yi +5
We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task…