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Hypothesis Testing the Circuit Hypothesis in LLMs Claudia Shi 1 Nicolas Beltran-V elez
Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented.
Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
In this paper, we introduce a set of spatial-temporal logic rules as knowledge to explain human actions.
abc1943857a42935ceacff03c524bb44-Paper-Conference.pdf
Unlike natural language processing and computer vision, the development of Foundation Models (FMs) for time series forecasting is blocked due to data scarcity.
Improving Subgroup Robustness via Data Selection
Machine learning models can often fail on subgroups that are underrepresented during training.
Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
Our approach combines three key ideas: (1) value-demand augmentation, (2) action-space approximate dynamic programming, and (3) time-space rounding.
