3 papers
cs.LG2025
Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
Greyson Brothers
We investigate the design of pooling methods used to summarize the outputs of transformer embedding models, primarily motivated by reinforcement learning and vision applications. T…
cs.LG2025
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL
Joshua McClellan, Greyson Brothers, Furong Huang +1
Equivariant Graph Neural Networks (EGNNs) have emerged as a promising approach in Multi-Agent Reinforcement Learning (MARL), leveraging symmetry guarantees to greatly improve sampl…
cs.CL2024
Uncovering Uncertainty in Transformer Inference
Greyson Brothers, Willa Mannering, Amber Tien +1
We explore the Iterative Inference Hypothesis (IIH) within the context of transformer-based language models, aiming to understand how a model's latent representations are progressi…