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ORCIDCharlotte Loh
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2020 – today
- 2024
[i9]Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljacic:
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation. CoRR abs/2406.00132 (2024)- [i8]Owen Dugan, Donato Manuel Jimenez Beneto, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljacic:
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step. CoRR abs/2406.06576 (2024) - 2023
- [j2]Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava:
Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. ICML 2023: 22614-22630 - [c4]Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng:
Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS 2023 - [c3]Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz:
Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS 2023 - [i7]Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. CoRR abs/2303.02484 (2023) - [i6]Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris N. Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava:
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies. CoRR abs/2304.00601 (2023) - [i5]Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljacic:
Multimodal Learning for Crystalline Materials. CoRR abs/2312.00111 (2023) - 2022
- [j1]Samuel Kim, Peter Y. Lu
, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. Trans. Mach. Learn. Res. 2022 (2022) - [c2]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. ICLR 2022 - [i4]Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj, Seungwook Han, Ligong Han, Leonid Karlinsky, Marin Soljacic, Akash Srivastava:
On the Importance of Calibration in Semi-supervised Learning. CoRR abs/2210.04783 (2022) - 2021
- [i3]Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. CoRR abs/2104.11667 (2021) - [i2]Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljacic:
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science. CoRR abs/2110.08406 (2021) - [i1]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Contrastive Learning. CoRR abs/2111.00899 (2021)
2010 – 2019
- 2011
- [c1]Laszlo Papp, Norbert Zsoter, Charlotte Loh, Baeumer Ole, Bernhard Egeler, Ildikó Garai, Ulf Luetzen:
Automated lymph node detection and classification on breast and prostate cancer SPECT-CT images. EMBC 2011: 3431-3434
Coauthor Index
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