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Hritik Bansal
Hritik Bansal
University of California Los Angeles | Indian Institute of Technology Delhi
Verified email at g.ucla.edu - Homepage
Title
Cited by
Cited by
Year
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
P Lu, H Bansal, T Xia, J Liu, C Li, H Hajishirzi, H Cheng, KW Chang, ...
arXiv preprint arXiv:2310.02255, 2023
316*2023
Cyclip: Cyclic contrastive language-image pretraining
S Goel, H Bansal, S Bhatia, R Rossi, V Vinay, A Grover
Advances in Neural Information Processing Systems 35, 6704-6719, 2022
1412022
How well can text-to-image generative models understand ethical natural language interventions?
H Bansal, D Yin, M Monajatipoor, KW Chang
arXiv preprint arXiv:2210.15230, 2022
792022
Leaving reality to imagination: Robust classification via generated datasets
H Bansal, A Grover
arXiv preprint arXiv:2302.02503, 2023
722023
An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement
U Gupta, H Bansal, D Joshi
Computer methods and programs in biomedicine 189, 105305, 2020
612020
Visit-bench: A benchmark for vision-language instruction following inspired by real-world use
Y Bitton, H Bansal, J Hessel, R Shao, W Zhu, A Awadalla, J Gardner, ...
arXiv preprint arXiv:2308.06595, 2023
552023
Geomlama: Geo-diverse commonsense probing on multilingual pre-trained language models
D Yin, H Bansal, M Monajatipoor, LH Li, KW Chang
arXiv preprint arXiv:2205.12247, 2022
502022
Rethinking the role of scale for in-context learning: An interpretability-based case study at 66 billion scale
H Bansal, K Gopalakrishnan, S Dingliwal, S Bodapati, K Kirchhoff, D Roth
arXiv preprint arXiv:2212.09095, 2022
462022
Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning
H Bansal, N Singhi, Y Yang, F Yin, A Grover, KW Chang
Proceedings of the IEEE/CVF International Conference on Computer Vision, 112-123, 2023
412023
Datacomp-lm: In search of the next generation of training sets for language models
J Li, A Fang, G Smyrnis, M Ivgi, M Jordan, S Gadre, H Bansal, E Guha, ...
arXiv preprint arXiv:2406.11794, 2024
38*2024
Dynosaur: A dynamic growth paradigm for instruction-tuning data curation
D Yin, X Liu, F Yin, M Zhong, H Bansal, J Han, KW Chang
arXiv preprint arXiv:2305.14327, 2023
362023
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
T Nguyen, R Shah, H Bansal, T Arcomano, R Maulik, V Kotamarthi, ...
arXiv preprint arXiv:2312.03876, 2023
32*2023
Generative verifiers: Reward modeling as next-token prediction
L Zhang, A Hosseini, H Bansal, M Kazemi, A Kumar, R Agarwal
arXiv preprint arXiv:2408.15240, 2024
222024
Climatelearn: Benchmarking machine learning for weather and climate modeling
T Nguyen, J Jewik, H Bansal, P Sharma, A Grover
Advances in Neural Information Processing Systems 36, 2024
202024
Peering through preferences: Unraveling feedback acquisition for aligning large language models
H Bansal, J Dang, A Grover
arXiv preprint arXiv:2308.15812, 2023
202023
Smaller, weaker, yet better: Training llm reasoners via compute-optimal sampling
H Bansal, A Hosseini, R Agarwal, VQ Tran, M Kazemi
arXiv preprint arXiv:2408.16737, 2024
152024
ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models
R Wadhawan, H Bansal, KW Chang, N Peng
arXiv preprint arXiv:2401.13311, 2024
92024
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?
G Bhatt, H Bansal, R Singh, S Agarwal
Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020
82020
VideoPhy: Evaluating Physical Commonsense for Video Generation
H Bansal, Z Lin, T Xie, Z Zong, M Yarom, Y Bitton, C Jiang, Y Sun, ...
arXiv preprint arXiv:2406.03520, 2024
72024
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
Y Wan, A Subramonian, A Ovalle, Z Lin, A Suvarna, C Chance, H Bansal, ...
arXiv preprint arXiv:2404.01030, 2024
72024
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