2 code implementations • 18 May 2023 • Benjamin Hoffman, Maddie Cusimano, Vittorio Baglione, Daniela Canestrari, Damien Chevallier, Dominic L. DeSantis, Lorène Jeantet, Monique A. Ladds, Takuya Maekawa, Vicente Mata-Silva, Víctor Moreno-González, Eva Trapote, Outi Vainio, Antti Vehkaoja, Ken Yoda, Katherine Zacarian, Ari Friedlaender
Animal-borne sensors ('bio-loggers') can record a suite of kinematic and environmental data, which can elucidate animal ecophysiology and improve conservation efforts.
1 code implementation • NeurIPS 2021 • Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
no code implementations • 12 Dec 2019 • Daisuke Ogawa, Toru Tamaki, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Ken Yoda
An efficient inverse reinforcement learning for generating trajectories is proposed based of 2D and 3D activity forecasting.