Genre
Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice
AI systems need exact descriptions of tasks to be performed.
Deep Learning Through AT elescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond Alan Jeffares
Deep learning sometimes appears to work in unexpected ways.
Persistent Test-time Adaptation in Recurring Testing Scenarios
Current test-time adaptation (TT A) approaches aim to adapt a machine learning model to environments that change continuously.
df22a19686a558e74f038e6277a51f68-Paper-Conference.pdf
In the classical decision-making literature, this is achieved by two interweaving processes, policyevaluation and policyimprovement (Sutton and Barto,2018).
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards
Relational Verification Leaps Forward with RABBit
Code is at this URL.