3 citations · 3 across the 4 of their papers we have counts for
4 papers
Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Gia Ancone, Qibin Liu, Liangyu Wu +1
Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the perform…
Physical Object Understanding with a Physically Controllable World Model
Rahul Venkatesh, Klemen Kotar, Lilian Naing Chen +9
A central challenge in visual intelligence is learning the physical structure of scenes from raw videos: how regions form objects and the laws that govern their interactions. Solvi…
CausalT5k: Diagnosing Refusal and Failure Modes in Trustworthy Causal Reasoning Across Causal Rungs
Longling Geng, Andy Ouyang, Theodore Wu +10
Large language models increasingly produce fluent causal explanations, yet they often fail in ways aggregate accuracy cannot diagnose: confusing association with intervention, aban…
Discovering and using Spelke segments
Rahul Venkatesh, Klemen Kotar, Lilian Naing Chen +10
Segments in computer vision are often defined by semantic considerations and are highly dependent on category-specific conventions. In contrast, developmental psychology suggests t…