Naoya Takeishi | DeepAI

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∙ 06/16/2023

Sample-Efficient On-Policy Imitation Learning from Observations

Imitation learning from demonstrations (ILD) aims to alleviate numerous ...

0 Joao A. Candido Ramos, et al. ∙

∙ 05/22/2023

Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations

Modeling of real-world biological multi-agents is a fundamental problem ...

0 Keisuke Fujii, et al. ∙

∙ 10/24/2022

Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models

The combination of deep neural nets and theory-driven models, which we c...

0 Naoya Takeishi, et al. ∙

∙ 06/04/2022

Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios

Evaluation of intervention in a multi-agent system, e.g., when humans sh...

0 Keisuke Fujii, et al. ∙

∙ 11/23/2021

Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks

Asteroid exploration has been attracting more attention in recent years....

0 Naoya Ozaki, et al. ∙

∙ 07/12/2021

Learning interaction rules from multi-animal trajectories via augmented behavioral models

Extracting the interaction rules of biological agents from moving sequen...

0 Keisuke Fujii, et al. ∙

∙ 02/25/2021

Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling

Integrating physics models within machine learning holds considerable pr...

10 Naoya Takeishi, et al. ∙

∙ 02/19/2021

Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections

Extracting coherent patterns is one of the standard approaches towards u...

0 Naoya Takeishi, et al. ∙

∙ 07/07/2020

Policy learning with partial observation and mechanical constraints for multi-person modeling

Extracting the rules of real-world biological multi-agent behaviors is a...

0 Keisuke Fujii, et al. ∙

∙ 06/16/2020

Learning Dynamics Models with Stable Invariant Sets

Stable invariant sets are an essential notion in the analysis and applic...

0 Naoya Takeishi, et al. ∙

∙ 04/09/2020

On Anomaly Interpretation via Shapley Values

Anomaly localization is an essential problem as anomaly detection is. Be...

0 Naoya Takeishi, et al. ∙

∙ 09/08/2019

Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection

We present a method to compute the Shapley values of reconstruction erro...

0 Naoya Takeishi, et al. ∙

∙ 05/13/2019

Physically-interpretable classification of network dynamics for complex collective motions

Understanding complex network dynamics is a fundamental issue in various...

0 Keisuke Fujii, et al. ∙

∙ 02/06/2019

Regularizing Generative Models Using Knowledge of Feature Dependence

Generative modeling is a fundamental problem in machine learning with ma...

0 Naoya Takeishi, et al. ∙

∙ 06/29/2018

Knowledge-Based Distant Regularization in Learning Probabilistic Models

Exploiting the appropriate inductive bias based on the knowledge of data...

0 Naoya Takeishi, et al. ∙

∙ 05/06/2018

Dynamic and Static Topic Model for Analyzing Time-Series Document Collections

For extracting meaningful topics from texts, their structures should be ...

0 Rem Hida, et al. ∙

∙ 11/28/2017

Recent Developments in Aerial Robotics: A Survey and Prototypes Overview

In recent years, research and development in aerial robotics (i.e., unma...

0 Chun Fui Liew, et al. ∙

∙ 10/12/2017

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

Spectral decomposition of the Koopman operator is attracting attention a...

0 Naoya Takeishi, et al. ∙

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