Operator learning is a data-driven approximation of mappings between infinite-dimensional function spaces, such as the solution operators of partial differentia...
Score-based diffusion models currently constitute the state of the art in continuous generative modeling. These methods are typically formulated via overdamped ...
Terrain-following coordinates in atmospheric models often imprint their grid structure onto the solution, particularly over steep topography, where distorted co...
A key challenge in evaluating VLMs is testing models' ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK...
Modern diffusion models (DMs) have achieved state-of-the-art image generation. However, the fundamental design choice of diffusing data all the way to white noi...
Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an i...
Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While...
Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can...
While Large Language Models (LLMs) have evolved into distinct platforms with unique interface designs and capabilities, existing public datasets treat models as...
Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language a...
Test and verification are essential activities in hardware and system design, but their complexity grows significantly with increasing system sizes. While Behav...
Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning...
