16 citations · 18 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…
Latent Concept Disentanglement in Transformer-based Language Models
Guan Zhe Hong, Bhavya Vasudeva, Vatsal Sharan +3
When large language models (LLMs) use in-context learning (ICL) to solve a new task, they must infer latent concepts from demonstration examples. This raises the question of whethe…
The Rich and the Simple: On the Implicit Bias of Adam and SGD
Bhavya Vasudeva, Jung Whan Lee, Vatsal Sharan +1
Adam is the de facto optimization algorithm for several deep learning applications, but an understanding of its implicit bias and how it differs from other algorithms, particularly…
Transformers Learn Low Sensitivity Functions: Investigations and Implications
Bhavya Vasudeva, Deqing Fu, Tianyi Zhou +3
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of their inductive biases and how those biases differ from other neural networ…