9 papers
Interpreting Language Model Hidden States at Scale
Jordan Pettyjohn, Mansi Sakarvadia, Nathaniel Hudson +3
Lens methods interpret large language models (LLMs) by mapping intermediate activations to the output vocabulary, revealing how next-token predictions develop through the network.…
The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
Mansi Sakarvadia, Kareem Hegazy, Amin Totounferoush +4
A core challenge in scientific machine learning, and scientific computing more generally, is modeling continuous phenomena which (in practice) are represented discretely. Machine-l…
Empowering Scientific Workflows with Federated Agents
Alok Kamatar, J. Gregory Pauloski, Yadu Babuji +5
Agentic systems, in which diverse agents cooperate to tackle challenging problems, are exploding in popularity in the AI community. However, existing agentic frameworks take a rela…
SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses
Sahil Sethi, Sai Reddy, Mansi Sakarvadia +4
Deep learning has shown strong performance in musculoskeletal imaging, but prior work has largely targeted conditions where diagnosis is relatively straightforward. More challengin…
Topology-Aware Knowledge Propagation in Decentralized Learning
Mansi Sakarvadia, Nathaniel Hudson, Tian Li +2
Decentralized learning enables collaborative training of models across naturally distributed data without centralized coordination or maintenance of a global model. Instead, device…
Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
Arham Khan, Todd Nief, Nathaniel Hudson +6
We survey the model merging literature through the lens of loss landscape geometry to connect observations from empirical studies on model merging and loss landscape analysis to ph…