16 citations · 28 across the 11 of their papers we have counts for
11 papers
Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge
Bhavya Vasudeva, Puneesh Deora, Alberto Bietti +2
Transformer-based language models excel at in-context learning (ICL), where they can adapt to new tasks based on contextual examples, without parameter updates. In a specific form…
Facts in Stats: Impacts of Pretraining Diversity on Language Model Generalization
Tina Behnia, Puneesh Deora, Christos Thrampoulidis
Language models are pretrained on sequences that blend statistical regularities (making text fluent) with factual associations between specific tokens (knowledge of facts). While r…
How Muon's Spectral Design Benefits Generalization: A Study on Imbalanced Data
Bhavya Vasudeva, Puneesh Deora, Yize Zhao +2
The growing adoption of spectrum-aware matrix-valued optimizers such as Muon and Shampoo in deep learning motivates a systematic study of their generalization properties and, in pa…
In-Context Occam's Razor: How Transformers Prefer Simpler Hypotheses on the Fly
Puneesh Deora, Bhavya Vasudeva, Tina Behnia +1
In-context learning (ICL) enables transformers to adapt to new tasks through contextual examples without parameter updates. While existing research has typically studied ICL in fix…
Implicit Bias and Fast Convergence Rates for Self-attention
Bhavya Vasudeva, Puneesh Deora, Christos Thrampoulidis
We study the fundamental optimization principles of self-attention, the defining mechanism of transformers, by analyzing the implicit bias of gradient-based optimizers in training…
On the Optimization and Generalization of Multi-head Attention
Puneesh Deora, Rouzbeh Ghaderi, Hossein Taheri +1
The training and generalization dynamics of the Transformer's core mechanism, namely the Attention mechanism, remain under-explored. Besides, existing analyses primarily focus on s…