Search Results for author: Charlotte Loh

Found 5 papers, 3 papers with code

On the Importance of Calibration in Semi-supervised Learning

no code implementations10 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.

Equivariant Contrastive Learning

2 code implementations28 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.

Contrastive Learning Self-Supervised Learning

Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science

1 code implementation15 Oct 2021 Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljacic

Deep learning techniques have been increasingly applied to the natural sciences, e. g., for property prediction and optimization or material discovery.

Contrastive Learning

Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations

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.

Self-Supervised Learning

Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure

1 code implementation23 Apr 2021 Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić

Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.

Gaussian Processes

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