ViT-K uses Vision Transformers and Koopman operators to learn stable long-term spatiotemporal dynamics of coupled fluid-porous media flows from sparse data.
Journal of machine learning research , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
PAC learning-based DR-MPC framework interpolates between robust MPC and stochastic MPC for interactive trajectory planning under agent decision uncertainty.
citing papers explorer
-
ViT-K: A Few-Shot Learning Model for Coupled Fluid-Porous Media Flows with Interface Conditions
ViT-K uses Vision Transformers and Koopman operators to learn stable long-term spatiotemporal dynamics of coupled fluid-porous media flows from sparse data.
-
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
-
Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
PAC learning-based DR-MPC framework interpolates between robust MPC and stochastic MPC for interactive trajectory planning under agent decision uncertainty.