Search Results for author:
Found 13 papers, 8 papers with code
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning
We propose a causal graph and design an encoder-decoder hypernetwork that explicitly disentangles invariant functions from environment-specific dynamics.
A Hierarchical Language Model For Interpretable Graph Reasoning
Large language models (LLMs) are being increasingly explored for graph tasks.
Geometry Informed Tokenization of Molecules for Language Model Generation
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries.
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
We consider achieving equivariance in machine learning systems via frame averaging.
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
Extensive experimental results confirm consistency with our theoretical analyses and show that the proposed ATTA method yields substantial performance improvements over TTA methods while maintaining efficiency and shares similar effectiveness to the more demanding active domain adaptation (ADA) methods.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution.
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs.
FlowX: Towards Explainable Graph Neural Networks via Message Flows
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms.
GOOD: A Graph Out-of-Distribution Benchmark
Our GOOD benchmark is a growing project and expects to expand in both quantity and variety of resources as the area develops.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
Explainability in Graph Neural Networks: A Taxonomic Survey
To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability.
FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation
In the first stage, deep structure-aware features are employed to predict feature flows from two consecutive frames to their intermediate result, and further generate the structure image of the intermediate frame.
Ranked #17 on
Video Frame Interpolation
on MSU Video Frame Interpolation
(LPIPS metric)
