Education
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data.
Noether's Razor: Learning Conserved Quantities
Symmetries have proven useful in machine learning models, improving generalisation and overall performance.
CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities [ Supplementary Material ]
To facilitate these annotations, we used both our user application and Label Studio.
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Unsupervised Environment Design (UED) formalizes the problem of autocur-ricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student's ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to
Can LLMs Solve Molecule Puzzles A Benchmark for Molecular Structure Elucidation
LLMs with improved chemical reasoning capabilities in the other two sub-tasks.
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
A new framework is proposed in this paper to address these issues.