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Found 4 papers, 2 papers with code
Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion
(iii) To generate more realistic behavior, RallyNet leverages Geometric Brownian Motion (GBM) to model the interactions between players by introducing a valuable inductive bias for learning player behaviors.
Root Cause Analysis In Microservice Using Neural Granger Causal Discovery
To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning.
NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis
In this work, we use CLIP to extract aligned image-text features and propose a novel meme sentiment analysis framework, consisting of a Cooperative Teaching Model (CTM) for Task A and a Cascaded Emotion Classifier (CEC) for Tasks B&C.
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement.
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