3 papers
cs.LG2025
Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation
Ari S. Benjamin, Kyle Daruwalla, Christian Pehle +2
One frequently wishes to learn a range of similar tasks as efficiently as possible, re-using knowledge across tasks. In artificial neural networks, this is typically accomplished b…
cs.CL2025
Token-Level Uncertainty-Aware Objective for Language Model Post-Training
Tingkai Liu, Ari S. Benjamin, Anthony M. Zador
In the current work, we connect token-level uncertainty in causal language modeling to two types of training objectives: 1) masked maximum likelihood (MLE), 2) self-distillation. W…
cs.LG2025
Continual learning with the neural tangent ensemble
Ari S. Benjamin, Christian Pehle, Kyle Daruwalla
A natural strategy for continual learning is to weigh a Bayesian ensemble of fixed functions. This suggests that if a (single) neural network could be interpreted as an ensemble, o…