630 citations · 2k across the 17 of their papers we have counts for
6 papers · 1 filter
Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models
Benjamin Hoover, Hendrik Strobelt, Dmitry Krotov +3
The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks. Though the generative process is traditionally understood as an…
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi, Judy Hoffman, Kate Saenko +2
Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to util…
Representation Learning Through Latent Canonicalizations
Or Litany, Ari Morcos, Srinath Sridhar +2
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision. Many prior approaches to this problem have foc…
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji, Tom Goldstein, Judy Hoffman
Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. Despite its success as a defense mechanism, adversar…
Algorithms and Theory for Multiple-Source Adaptation
Judy Hoffman, Mehryar Mohri, Ningshan Zhang
This work includes a number of novel contributions for the multiple-source adaptation problem. We present new normalized solutions with strong theoretical guarantees for the cross-…
Multiple-Source Adaptation for Regression Problems
Judy Hoffman, Mehryar Mohri, Ningshan Zhang
We present a detailed theoretical analysis of the problem of multiple-source adaptation in the general stochastic scenario, extending known results that assume a single target labe…