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Jing Huang
Jing Huang
Dirección de correo verificada de stanford.edu
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Learning two-branch neural networks for image-text matching tasks
L Wang, Y Li, J Huang, S Lazebnik
IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2), 394-407, 2018
5802018
Improving weakly supervised visual grounding by contrastive knowledge distillation
L Wang, J Huang, Y Li, K Xu, Z Yang, D Yu
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
752021
Causal abstraction: A theoretical foundation for mechanistic interpretability
A Geiger, D Ibeling, A Zur, M Chaudhary, S Chauhan, J Huang, A Arora, ...
arXiv:2301.04709, 2024
54*2024
Culturally aware natural language inference
J Huang, D Yang
Findings of the Association for Computational Linguistics: EMNLP 2023, 7591-7609, 2023
292023
Rigorously Assessing Natural Language Explanations of Neurons
J Huang, A Geiger, K D'Oosterlinck, Z Wu, C Potts
arXiv preprint arXiv:2309.10312, 2023
272023
RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
J Huang, Z Wu, C Potts, M Geva, A Geiger
arXiv preprint arXiv:2402.17700, 2024
122024
pyvene: A library for understanding and improving PyTorch models via interventions
Z Wu, A Geiger, A Arora, J Huang, Z Wang, ND Goodman, CD Manning, ...
arXiv preprint arXiv:2403.07809, 2024
112024
Demystifying Verbatim Memorization in Large Language Models
J Huang, D Yang, C Potts
arXiv preprint arXiv:2407.17817, 2024
92024
Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training
J Huang, Z Wu, K Mahowald, C Potts
arXiv preprint arXiv:2212.09897, 2022
82022
A Reply to Makelov et al.(2023)'s" Interpretability Illusion" Arguments
Z Wu, A Geiger, J Huang, A Arora, T Icard, C Potts, ND Goodman
arXiv preprint arXiv:2401.12631, 2024
32024
Systems And Methods For Training Translation Models Using Source-Augmented Training Examples
J Huang, A Shah, M Johnson, V Ratnakar, M Krikun
US Patent App. 17/988,315, 2023
2023
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