28 citations · 28 across the 5 of their papers we have counts for
6 papers
Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Valuing player actions in football requires accounting for strategic interactions among 22 players, including off-ball movements and defensive positioning. Existing reinforcement-l…
Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Invasion team sports such as soccer produce a high-dimensional, strongly coupled state space as many players continuously interact on a shared field, challenging quantitative tacti…
Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Understanding football tactics is crucial for managers and analysts. Previous research has proposed models based on spatial and kinematic equations, but these are computationally e…
Information Locality as an Inductive Bias for Neural Language Models
Taiga Someya, Anej Svete, Brian DuSell +3
Inductive biases are inherent in every machine learning system, shaping how models generalize from finite data. In the case of neural language models (LMs), debates persist as to w…
OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer
Calvin Yeung, Kenjiro Ide, Taiga Someya +1
Sports analytics has become both more professional and sophisticated, driven by the growing availability of detailed performance data. This progress enables applications such as ma…
SoccerNet 2023 Challenges Results
Anthony Cioppa, Silvio Giancola, Vladimir Somers +99
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vi…