| Equivariant Contrastive Learning R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... arXiv preprint arXiv:2111.00899, 2021 | 105 | 2021 |
| Predictive and generative machine learning models for photonic crystals T Christensen, C Loh, S Picek, D Jakobović, L Jing, S Fisher, V Ceperic, ... Nanophotonics 9 (13), 4183-4192, 2020 | 85 | 2020 |
| Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljacic Transactions of Machine Learning Research, 2021 | 16 | 2021 |
| Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljačić arXiv preprint arXiv:2104.11667, 2021 | 15 | 2021 |
| Surrogate-and invariance-boosted contrastive learning for data-scarce applications in science C Loh, T Christensen, R Dangovski, S Kim, M Soljačić Nature Communications 13 (1), 1-12, 2022 | 10 | 2022 |
| Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies L Han, S Han, S Sudalairaj, C Loh, R Dangovski, F Deng, P Agrawal, ... arXiv preprint arXiv:2304.00601, 2023 | 4 | 2023 |
| Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries C Loh, S Han, S Sudalairaj, R Dangovski, K Xu, F Wenzel, M Soljacic, ... International Conference on Machine Learning, 2023 | 3 | 2023 |
| On the Importance of Calibration in Semi-supervised Learning C Loh, R Dangovski, S Sudalairaj, S Han, L Han, L Karlinsky, M Soljacic, ... arXiv preprint arXiv:2210.04783, 2022 | 2 | 2022 |
| Multimodal Learning for Crystalline Materials V Moro, C Loh, R Dangovski, A Ghorashi, A Ma, Z Chen, PY Lu, ... arXiv preprint arXiv:2312.00111, 2023 | 1 | 2023 |
| Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging C Loh, R Dangovski, S Sudalairaj, S Han, L Han, L Karlinsky, M Soljacic, ... Transactions on Machine Learning Research, 2023 | 1 | 2023 |
| Overcoming Data Scarcity in Deep Learning of Scientific Problems CCL Loh Massachusetts Institute of Technology, 2021 | 1 | 2021 |
| Analyzing Generalization of Neural Networks through Loss Path Kernels Y Chen, W Huang, H Wang, C Loh, A Srivastava, L Nguyen, L Weng Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Towards robust and generalizable representations of extracellular data using contrastive learning A Vishnubhotla, C Loh, A Srivastava, L Paninski, C Hurwitz Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Phase Transitions in Contrastive Learning A Cy, A Chemparathy, M Han, R Dangovski, PY Lu, C Loh, M Soljacic | | 2023 |
| Deep Learning for Bayesian Optimization of High-Dimensional Scientific Problems S Kim, P Lu, C Loh, M Soljačić, J Snoek, J Smith Bulletin of the American Physical Society, 2022 | | 2022 |
| Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... International Conference on Learning Representations, 2021 | | 2021 |
| Supplementary Materials: Towards robust and generalizable representations of extracellular data using contrastive learning A Vishnubhotla, C Loh, L Paninski, A Srivastava, C Hurwitz | | |
| On Assimilating Learned Views in Contrastive Learning L Han, S Han, S Sudalairaj, C Loh, R Dangovski, P Agrawal, DN Metaxas, ... | | |
