most citedFeature Impact Analysis on Top Long-Jump Performances with Quantile Random Forest and Explainable AI Techniques

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

Can LLMs Translate Human Instructions into a Reinforcement Learning Agent's Internal Emergent Symbolic Representation?

Ziqi Ma, Sao Mai Nguyen, Philippe Xu

Emergent symbolic representations are critical for enabling developmental learning agents to plan and generalize across tasks. In this work, we investigate whether large language m…

cs.LG2025★ 1 cited

Feature Impact Analysis on Top Long-Jump Performances with Quantile Random Forest and Explainable AI Techniques

Qi Gan, Stephan Clémençon, Mounîm A. El-Yacoubi +3

Biomechanical features have become important indicators for evaluating athletes' techniques. Traditionally, experts propose significant features and evaluate them using physics equ…

cs.CV2025

Polar Coordinate-Based 2D Pose Prior with Neural Distance Field

Qi Gan, Sao Mai Nguyen, Eric Fenaux +2

Human pose capture is essential for sports analysis, enabling precise evaluation of athletes' movements. While deep learning-based human pose estimation (HPE) models from RGB video…

cs.HC2025

Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises

Aleksa Marusic, Sao Mai Nguyen, Adriana Tapus

Physical rehabilitation exercises suggested by healthcare professionals can help recovery from various musculoskeletal disorders and prevent re-injury. However, patients' engagemen…

cs.CY2024

Prerequisite Structure Discovery in Intelligent Tutoring Systems

Louis Annabi, Sao Mai Nguyen

This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. T…

cs.LG2024

Reconciling Spatial and Temporal Abstractions for Goal Representation

Mehdi Zadem, Sergio Mover, Sao Mai Nguyen

Goal representation affects the performance of Hierarchical Reinforcement Learning (HRL) algorithms by decomposing the complex learning problem into easier subtasks. Recent studies…