Imitation learning (IL) enables autonomous behavior by learning from expert demonstrations. While more sample-efficient than comparative alternatives like reinf...
Over a billion users across the globe interact with AI systems engineered with increasing sophistication to mimic human traits. This shift has triggered urgent ...
We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-...
Neural Quantum States (NQS) use neural networks to represent wavefunctions of quantum many-body systems, but their performance depends on the choice of basis, y...
Current approaches for designing self-explainable models (SEMs) require complicated training procedures and specific architectures which makes them impractical....
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...
