15 citations · 32 across the 7 of their papers we have counts for
6 papers · 1 filter
Geometry-Aware Adaptation for Pretrained Models
Nicholas Roberts, Xintong Li, Dyah Adila +4
Machine learning models -- including prominent zero-shot models -- are often trained on datasets whose labels are only a small proportion of a larger label space. Such spaces are c…
Lifting Weak Supervision To Structured Prediction
Harit Vishwakarma, Nicholas Roberts, Frederic Sala
Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from a variety of sources. WS is th…
AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad +7
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate c…
Rethinking Neural Operations for Diverse Tasks
Nicholas Roberts, Mikhail Khodak, Tri Dao +3
An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users…
Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker
Nicholas Roberts, Vinay Uday Prabhu, Matthew McAteer
This paper explores the scenarios under which an attacker can claim that 'Noise and access to the softmax layer of the model is all you need' to steal the weights of a convolutiona…
Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity
Dian Ang Yap, Nicholas Roberts, Vinay Uday Prabhu
Kernel sparsity ("dying ReLUs") and lack of diversity are commonly observed in CNN kernels, which decreases model capacity. Drawing inspiration from information theory and wireless…