575 citations
- University of WashingtonUS32 papers
- Carnegie Mellon UniversityUS21 papers
- Allen Institute for Brain ScienceUS16 papers
- Allen Institute for Artificial IntelligenceUS11 papers
- Stanford UniversityUS7 papers
- Johns Hopkins UniversityUS5 papers
- Massachusetts Institute of TechnologyUS5 papers
- California Institute of TechnologyUS4 papers
- Google (United States)US4 papers
- Harvard UniversityUS4 papers
- University of TorontoCA4 papers
- Cold Spring Harbor LaboratoryUS3 papers
5 papers · 1 filter
Surfacing Semantic Orthogonality Across Model Safety Benchmarks: A Multi-Dimensional Analysis
Jonathan Bennion, Shaona Ghosh, Mantek Singh +1
Various AI safety datasets have been developed to measure LLMs against evolving interpretations of harm. Our evaluation of five recently published open-source safety benchmarks rev…
Attention for Causal Relationship Discovery from Biological Neural Dynamics
Ziyu Lu, Anika Tabassum, Shruti Kulkarni +4
This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biop…
Learning Neural Network Subspaces
Mitchell Wortsman, Maxwell Horton, Carlos Guestrin +2
Recent observations have advanced our understanding of the neural network optimization landscape, revealing the existence of (1) paths of high accuracy containing diverse solutions…
Mitigating Bias in Calibration Error Estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens +1
For an AI system to be reliable, the confidence it expresses in its decisions must match its accuracy. To assess the degree of match, examples are typically binned by confidence an…
Complementary reinforcement learning towards explainable agents
Jung Hoon Lee
Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. T…