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20172022
most citedConnecting Weighted Automata and Recurrent Neural Networks through Spectral Learning

26 citations · 64 across the 16 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.RO2020

Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Joanne Truong, Denis Yarats, Tianyu Li +4

Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation po…

cs.IR2020★ 4 cited

Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization

Ramin Raziperchikolaei, Tianyu Li, Young-joo Chung

Autoencoder-based hybrid recommender systems have become popular recently because of their ability to learn user and item representations by reconstructing various information sour…

cs.LG2020★ 1 cited

Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning

Tianyu Li, Doina Precup, Guillaume Rabusseau

In this paper, we present connections between three models used in different research fields: weighted finite automata~(WFA) from formal languages and linguistics, recurrent neural…

cs.LG2020★ 1 cited

Learning to Profile: User Meta-Profile Network for Few-Shot Learning

Hao Gong, Qifang Zhao, Tianyu Li +2

Meta-learning approaches have shown great success in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications…

cs.RO2020★ 3 cited

Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion

Tianyu Li, Roberto Calandra, Deepak Pathak +3

Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional "latent" action u…

cs.LG2020★ 1 cited

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces

Bogdan Mazoure, Thang Doan, Tianyu Li +4

We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducin…