The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy waste has raised c...
As the online learning landscape evolves, the need for personalization is increasingly evident. Although educational resources are burgeoning, educators face ch...
Large-language models (LLMs) have been shown to respond in a variety of ways for classification tasks outside of question-answering. LLM responses are sometimes...
Dexterous manipulation is challenging because it requires understanding how subtle hand motion influences the environment through contact with objects. We intro...
The validation and verification of artificial intelligence (AI) models through robustness assessment are essential to guarantee the reliable performance of inte...
We consider statistical tasks in high dimensions whose loss depends on the data only through its projection into a fixed-dimensional subspace spanned by the par...
Stuttering detection breaks down when disfluencies overlap. Existing parametric models struggle to distinguish complex, simultaneous disfluencies (e.g., a 'bloc...
Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategi...
Graph Neural Networks have demonstrated significant success in graph classification tasks, yet they often require substantial computational resources and strugg...
We introduce the Do-Undo task and benchmark to address a critical gap in vision-language models: understanding and generating physically plausible scene transfo...
Building general-purpose reasoning models with reinforcement learning (RL) entails substantial cross-domain heterogeneity, including large variation in inferenc...
Recent deep learning frameworks in histopathology, particularly multiple instance learning (MIL) combined with pathology foundational models (PFMs), have shown ...
