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Found 18 papers, 6 papers with code
Physically-interpretable classification of biological network dynamics for complex collective motions
A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties.
Learning interaction rules from multi-animal trajectories via augmented behavioral models
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence.
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections
For extracting meaningful topics from texts, their structures should be considered properly.
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Spectral decomposition of the Koopman operator is attracting attention as a tool for the analysis of nonlinear dynamical systems.
Knowledge-Based Distant Regularization in Learning Probabilistic Models
Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning.
Knowledge-Based Regularization in Generative Modeling
Prior domain knowledge can greatly help to learn generative models.
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection
We present a method to compute the Shapley values of reconstruction errors of principal component analysis (PCA), which is particularly useful in explaining the results of anomaly detection based on PCA.
Learning Dynamics Models with Stable Invariant Sets
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.
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields.
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
A key technical challenge is to strike a balance between the incomplete physics and trainable components such as neural networks for ensuring that the physics part is used in a meaningful manner.
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks
One of the mission design solutions is utilizing asteroid flyby cycler trajectories with multiple Earth gravity assists.
Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
Evaluation of intervention in a multiagent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
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 call deep grey-box modeling, can be inherently interpretable to some extent thanks to the theory backbone.
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines.
Mimicking Better by Matching the Approximate Action Distribution
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations.
