Search Results for author: Takuma Yoneda

Found 6 papers, 1 papers with code

Invariance Through Latent Alignment

no code implementations15 Dec 2021 Takuma Yoneda, Ge Yang, Matthew R. Walter, Bradly Stadie

A robot's deployment environment often involves perceptual changes that differ from what it has experienced during training.

Data Augmentation

Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge

2 code implementations8 Jan 2021 Takuma Yoneda, Charles Schaff, Takahiro Maeda, Matthew Walter

This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge. com/).

Motion Planning

Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman

no code implementations ICML Workshop LaReL 2020 Takuma Yoneda, Matthew R. Walter, Jason Naradowsky

In this work we perform a controlled study of human language use in a competitive team-based game, and search for useful lessons for structuring communication protocol between autonomous agents.

UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)

no code implementations WS 2018 Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, Sebastian Riedel

In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62. 52{\%} on the provisional test set (without additional human evaluation), and 65. 41{\%} on the development set.

Information Retrieval Natural Language Inference +1

Bib2vec: An Embedding-based Search System for Bibliographic Information

no code implementations16 Jun 2017 Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki

We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.

Bib2vec: Embedding-based Search System for Bibliographic Information

no code implementations EACL 2017 Takuma Yoneda, Koki Mori, Makoto Miwa, Yutaka Sasaki

We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility.

Network Embedding Topic Models

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