33 citations · 125 across the 19 of their papers we have counts for
19 papers
Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees
Calvin Yeung, Rory Bunker, Rikuhei Umemoto +1
Machine learning models have become increasingly popular for predicting the results of soccer matches, however, the lack of publicly-available benchmark datasets has made model eva…
A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory
Calvin C. K. Yeung, Keisuke Fujii
Complex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategie…
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda +1
Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game sit…
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Keisuke Fujii, Kazushi Tsutsui, Atom Scott +3
Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generat…
Estimation of control area in badminton doubles with pose information from top and back view drone videos
Ning Ding, Kazuya Takeda, Wenhui Jin +2
The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In doubles matches, coordinated positionin…
Learning to Predict Navigational Patterns from Partial Observations
Robin Karlsson, Alexander Carballo, Francisco Lepe-Salazar +3
Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lane…
Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis
Calvin C. K. Yeung, Tony Sit, Keisuke Fujii
With recently available football match event data that record the details of football matches, analysts and researchers have a great opportunity to develop new performance metrics,…
Predictive World Models from Real-World Partial Observations
Robin Karlsson, Alexander Carballo, Keisuke Fujii +2
Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learnin…
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii
Automatic fault detection is a major challenge in many sports. In race walking, referees visually judge faults according to the rules. Hence, ensuring objectivity and fairness whil…
Pitching strategy evaluation via stratified analysis using propensity score
Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii
Recent measurement technologies enable us to analyze baseball at higher levels. There are, however, still many unclear points around the pitching strategy. The two elements make it…
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda +1
Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game…
Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii
In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure…
Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi +3
Evaluation of intervention in a multiagent system, e.g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is c…
How does AI play football? An analysis of RL and real-world football strategies
Atom Scott, Keisuke Fujii, Masaki Onishi
Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications. Simulations using such agents can p…
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment
Robin Karlsson, Tomoki Hayashi, Keisuke Fujii +3
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimit…
Learning interaction rules from multi-animal trajectories via augmented behavioral models
Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui +10
Extracting the interaction rules of biological agents from movement sequences pose challenges in various domains. Granger causality is a practical framework for analyzing the inter…
Physically-interpretable classification of biological network dynamics for complex collective motions
Keisuke Fujii, Naoya Takeishi, Motokazu Hojo +2
Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between eleme…
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
Keisuke Fujii, Yoshinobu Kawahara
Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety of engineering and scientific fields. Dynamic mode decomposition (DMD), which is a numerical algorithm…
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda +2
The development of a metric for structural data is a long-term problem in pattern recognition and machine learning. In this paper, we develop a general metric for comparing nonline…