101 citations · 314 across the 13 of their papers we have counts for
16 papers
Tsubame: Tree Replay for Diffusion-Based Speculative Decoding
Yepeng Weng, Qiao Hu, Takehisa Yairi
Context-aware dynamic trees allocate the speculative decoding budget according to draft path probabilities, adapting their depth and branching to the current context. Under stochas…
RheoSampling: Resolving the One-Hot Dilemma in Stochastic Dynamic-Tree Speculative Decoding
Qiao Hu, Yepeng Weng, Bo Zhang +1
Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hierarchical structures. Dynami…
Self-Augmented Diffusion Guidance for Physics-Informed Generation
Akira Osaka, Naoya Takeishi, Takehisa Yairi
Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major limitation of standard diffusi…
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…
Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics
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, which provi…
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…
A Temporal Difference Method for Stochastic Continuous Dynamics
Haruki Settai, Naoya Takeishi, Takehisa Yairi
For continuous systems modeled by dynamical equations such as ODEs and SDEs, Bellman's Principle of Optimality takes the form of the Hamilton-Jacobi-Bellman (HJB) equation, which p…
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…
VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments
Koji Minoda, Fabian Schilling, Valentin Wüest +2
Dynamic environments such as urban areas are still challenging for popular visual-inertial odometry (VIO) algorithms. Existing datasets typically fail to capture the dynamic nature…
Companion Unmanned Aerial Vehicles: A Survey
Chun Fui Liew, Takehisa Yairi
Recent technological advancements in small-scale unmanned aerial vehicles (UAVs) have led to the development of companion UAVs. Similar to conventional companion robots, companion…
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections
Rem Hida, Naoya Takeishi, Takehisa Yairi +1
For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection…
Recent Developments in Aerial Robotics: A Survey and Prototypes Overview
Chun Fui Liew, Danielle DeLatte, Naoya Takeishi +1
In recent years, research and development in aerial robotics (i.e., unmanned aerial vehicles, UAVs) has been growing at an unprecedented speed, and there is a need to summarize the…
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
Spectral decomposition of the Koopman operator is attracting attention as a tool for the analysis of nonlinear dynamical systems. Dynamic mode decomposition is a popular numerical…
Subspace dynamic mode decomposition for stochastic Koopman analysis
Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
The analysis of nonlinear dynamical systems based on the Koopman operator is attracting attention in various applications. Dynamic mode decomposition (DMD) is a data-driven algorit…