3 citations · 7 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Mitigating Memorization In Language Models
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +6
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that d…
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
Nathaniel Hudson, Valerie Hayot-Sasson, Yadu Babuji +5
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks…
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
Ashka Shah, Adela DePavia, Nathaniel Hudson +2
The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us…
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…
Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices
Samir Rajani, Dario Dematties, Nathaniel Hudson +4
Federated learning (FL) is increasingly becoming the default approach for training machine learning models across decentralized Internet-of-Things (IoT) devices. A key advantage of…
Hierarchical and Decentralised Federated Learning
Omer Rana, Theodoros Spyridopoulos, Nathaniel Hudson +4
Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, th…