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ORCIDNathan Ng 0001
Person information
- affiliation: New York University, NY, USA
- affiliation (PhD 2024): University of Toronto, ON, Canada
- affiliation (until 2024): Massachusetts Institute of Technology, Cambridge, MA, USA
- affiliation (former): Facebook AI Research, USA
- affiliation (former): University of California, San Diego, CA, USA
Other persons with the same name
- Nathan Ng 0002
![[0009-0004-8508-8124]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0009-0004-8508-8124")
— University of Massachusetts Amherst, MA, USA (and 1 more) - Nathan Ng 0003 — University of Edinburgh, College of Medicine and Veterinary Medicine, UK
- Nathan Ng 0004 — University of Lethbridge, Canada
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2020 – today
- 2024
[b1]Nathan Ng:
Robustness and Invariance in Neural Networks. University of Toronto, Canada, 2024- [j3]Nathan H. Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho:
Blind Biological Sequence Denoising with Self-Supervised Set Learning. Trans. Mach. Learn. Res. 2024 (2024) - [j2]Kyle O'Brien, Nathan Hoyen Ng, Isha Puri, Jorge Mendez-Mendez, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi, Thomas Hartvigsen:
Improving Black-box Robustness with In-Context Rewriting. Trans. Mach. Learn. Res. 2024 (2024) - [c8]Nathan H. Ng, Roger Baker Grosse, Marzyeh Ghassemi:
Measuring Stochastic Data Complexity with Boltzmann Influence Functions. ICML 2024 - [i12]Kyle O'Brien, Nathan Ng, Isha Puri, Jorge Mendez, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi, Thomas Hartvigsen:
Improving Black-box Robustness with In-Context Rewriting. CoRR abs/2402.08225 (2024) - [i11]Nathan Ng, Roger B. Grosse, Marzyeh Ghassemi:
Measuring Stochastic Data Complexity with Boltzmann Influence Functions. CoRR abs/2406.02745 (2024) - 2023
- [j1]Nathan H. Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi:
Predicting Out-of-Domain Generalization with Neighborhood Invariance. Trans. Mach. Learn. Res. 2023 (2023) - [i10]Nathan Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho:
Blind Biological Sequence Denoising with Self-Supervised Set Learning. CoRR abs/2309.01670 (2023) - 2022
- [c7]Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? NeurIPS 2022 - [i9]Nathan Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi:
Predicting Out-of-Domain Generalization with Local Manifold Smoothness. CoRR abs/2207.02093 (2022) - [i8]Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? CoRR abs/2209.05364 (2022) - 2020
- [c6]Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi:
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness. EMNLP (1) 2020: 1268-1283 - [i7]Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi:
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness. CoRR abs/2009.10195 (2020) - [i6]Nathan Ng, Marzyeh Ghassemi, Narendran Thangarajan, Jiacheng Pan, Qi Guo:
Improving Dialogue Breakdown Detection with Semi-Supervised Learning. CoRR abs/2011.00136 (2020)
2010 – 2019
- 2019
- [c5]Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian J. McAuley, Zachary C. Lipton:
Embryo Staging with Weakly-Supervised Region Selection and Dynamically-Decoded Predictions. MLHC 2019: 663-679 - [c4]Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli:
fairseq: A Fast, Extensible Toolkit for Sequence Modeling. NAACL-HLT (Demonstrations) 2019: 48-53 - [c3]Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov:
Facebook FAIR's WMT19 News Translation Task Submission. WMT (2) 2019: 314-319 - [i5]Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli:
fairseq: A Fast, Extensible Toolkit for Sequence Modeling. CoRR abs/1904.01038 (2019) - [i4]Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian J. McAuley, Zachary C. Lipton:
Embryo staging with weakly-supervised region selection and dynamically-decoded predictions. CoRR abs/1904.04419 (2019) - [i3]Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov:
Facebook FAIR's WMT19 News Translation Task Submission. CoRR abs/1907.06616 (2019) - [i2]Kyra Yee, Nathan Ng, Yann N. Dauphin, Michael Auli:
Simple and Effective Noisy Channel Modeling for Neural Machine Translation. CoRR abs/1908.05731 (2019) - 2018
- [c2]Nathan H. Ng, Julian J. McAuley, Julian Gingold, Nina Desai, Zachary C. Lipton:
Predicting Embryo Morphokinetics in Videos with Late Fusion Nets & Dynamic Decoders. ICLR (Workshop) 2018 - 2017
- [c1]Nathan H. Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. MLHC 2017: 100-111 - [i1]Nathan Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. CoRR abs/1702.05386 (2017)
Coauthor Index
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