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20152022
most citedBeyond Sparsity: Tree Regularization of Deep Models for Interpretability

104 citations · 209 across the 10 of their papers we have counts for

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17 papers · 1 filter

cs.LG20221 cited

Know Thy Student: Interactive Learning with Gaussian Processes

Rose E. Wang, Mike Wu, Noah Goodman

Learning often involves interaction between multiple agents. Human teacher-student settings best illustrate how interactions result in efficient knowledge passing where the teacher…

cs.LG202110 cited

Temperature as Uncertainty in Contrastive Learning

Oliver Zhang, Mike Wu, Jasmine Bayrooti +1

Contrastive learning has demonstrated great capability to learn representations without annotations, even outperforming supervised baselines. However, it still lacks important prop…

cs.LG2021

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

Mike Wu, Noah Goodman, Stefano Ermon

In traditional software programs, it is easy to trace program logic from variables back to input, apply assertion statements to block erroneous behavior, and compose programs toget…

cs.LG20204 cited

A Simple Framework for Uncertainty in Contrastive Learning

Mike Wu, Noah Goodman

Contrastive approaches to representation learning have recently shown great promise. In contrast to generative approaches, these contrastive models learn a deterministic encoder wi…

cs.LG202026 cited

Conditional Negative Sampling for Contrastive Learning of Visual Representations

Mike Wu, Milan Mosse, Chengxu Zhuang +2

Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between tw…

cs.LG2020

HarperValleyBank: A Domain-Specific Spoken Dialog Corpus

Mike Wu, Jonathan Nafziger, Anthony Scodary +1

We introduce HarperValleyBank, a free, public domain spoken dialog corpus. The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 hum…