7 papers
Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
Henrik Krauss, Johann Licher, Naoya Takeishi +2
Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumptions. Model-based approaches require p…
Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Games
Henrik Krauss, Takehisa Yairi
We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with sy…
UniVer: A Unified Perspective for Multi-step and Multi-draft Speculative Decoding
Yepeng Weng, Qiao Hu, Takehisa Yairi
Speculative decoding accelerates Large Language Models via draft-then-verify, where verification can be framed as an Optimal Transport (OT) problem. Existing approaches typically h…
Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models
Kaito Goto, Naoya Takeishi, Takehisa Yairi
Converting betting odds into accurate outcome probabilities is a fundamental challenge in order to use betting odds as a benchmark for sports forecasting and market efficiency anal…
Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning
Henrik Krauss, Takehisa Yairi
This study introduces a novel method for revealing human internal attention patterns (decision-relevant attention) from gameplay data alone, leveraging offline attention techniques…
Accurate Open-Loop Control of a Soft Continuum Robot Through Visually Learned Latent Representations
Henrik Krauss, Johann Licher, Naoya Takeishi +2
This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, that provid…