no code implementations • ICML Workshop Deep_Phenomen 2019 • Chad DeChant, Seungwook Han, Hod Lipson
We show that information about whether a neural network's output will be correct or incorrect is present in the outputs of the network's intermediate layers.
no code implementations • 9 Sep 2020 • Seungwook Han, Akash Srivastava, Cole Hurwitz, Prasanna Sattigeri, David D. Cox
First, we generate images in low-frequency bands by training a sampler in the wavelet domain.
no code implementations • 1 Jan 2021 • Seungwook Han, Akash Srivastava, Cole Lincoln Hurwitz, Prasanna Sattigeri, David Daniel Cox
First, we generate images in low-frequency bands by training a sampler in the wavelet domain.
no code implementations • ICLR 2022 • Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
no code implementations • 29 Sep 2021 • Akash Srivastava, Seungwook Han, Benjamin Rhodes, Kai Xu, Michael U. Gutmann
As such, estimating density ratios accurately using only samples from $p$ and $q$ is of high significance and has led to a flurry of recent work in this direction.
2 code implementations • 28 Oct 2021 • Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljačić
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
no code implementations • 10 Oct 2022 • Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency regularization and pseudo-labeling.
1 code implementation • 4 Mar 2023 • Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava
In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters.
no code implementations • 2 Apr 2023 • Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava
Recently, several attempts have been made to replace such domain-specific, human-designed transformations with generated views that are learned.
no code implementations • 1 May 2023 • Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
We show that if these auxiliary densities are constructed such that they overlap with $p$ and $q$, then a multi-class logistic regression allows for estimating $\log p/q$ on the domain of any of the $K+2$ distributions and resolves the distribution shift problems of the current state-of-the-art methods.