101 citations · 346 across the 24 of their papers we have counts for
14 papers · 1 filter
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…
Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
Keisuke Fujii, Kazushi Tsutsui, Yu Teshima +6
Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal m…
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…
Mimicking Better by Matching the Approximate Action Distribution
João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi +1
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be deri…
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models
Naoya Takeishi, Alexandros Kalousis
The combination of deep neural nets and theory-driven models, which we call deep grey-box modeling, can be inherently interpretable to some extent thanks to the theory backbone. De…
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…
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Naoya Takeishi, Keisuke Fujii, Koh Takeuchi +1
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data. Dynamic mode decomposition (DMD) is a powerful tool for extracting cohere…
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Naoya Takeishi, Alexandros Kalousis
Integrating physics models within machine learning models holds considerable promise toward learning robust models with improved interpretability and abilities to extrapolate. In t…
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara +1
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observati…
Learning Dynamics Models with Stable Invariant Sets
Naoya Takeishi, Yoshinobu Kawahara
Invariance and stability are essential notions in dynamical systems study, and thus it is of great interest to learn a dynamics model with a stable invariant set. However, existing…
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
Naoya Takeishi, Yoshinobu Kawahara
In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for…
Knowledge-Based Regularization in Generative Modeling
Naoya Takeishi, Yoshinobu Kawahara
Prior domain knowledge can greatly help to learn generative models. However, it is often too costly to hard-code prior knowledge as a specific model architecture, so we often have…
Knowledge-Based Distant Regularization in Learning Probabilistic Models
Naoya Takeishi, Kosuke Akimoto
Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the doma…
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…