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Conference and Workshop Papers
- 2024
[c3]Yiming Zhang, Hantao Yang, Haobo Wang, Jake Zhao:
Fast Adaptation via Prompted Data: An Efficient Cross-Domain Fine-tuning Method for Large Language Models. LREC/COLING 2024: 7117-7132- 2022
- [c2]Ru Peng, Yawen Zeng, Jake Zhao:
Distill The Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation. EMNLP 2022: 2379-2390 - 2019
- [c1]Jake Zhao, Kyunghyun Cho:
Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples. CVPR 2019: 11563-11571
Informal and Other Publications
- 2022
- [i7]Yan Li, Hao Chen, Jake Zhao, Haolan Zhang, Jinpeng Li:
Benchmarking Domain Generalization on EEG-based Emotion Recognition. CoRR abs/2204.09016 (2022) - 2021
- [i6]Jimin Tan, Jianan Yang, Sai Wu, Gang Chen, Jake Zhao:
A critical look at the current train/test split in machine learning. CoRR abs/2106.04525 (2021) - [i5]Jake Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu, Gang Chen:
Joining datasets via data augmentation in the label space for neural networks. CoRR abs/2106.09260 (2021) - 2020
- [i4]Daniel Chin, Yian Zhang, Tianyu Zhang, Jake Zhao, Gus Xia:
Interactive Rainbow Score: A Visual-centered Multimodal Flute Tutoring System. CoRR abs/2004.13908 (2020) - 2019
- [i3]Jiatao Gu, Changhan Wang, Jake Zhao:
Levenshtein Transformer. CoRR abs/1905.11006 (2019) - 2018
- [i2]Jake Zhao, Kyunghyun Cho:
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples. CoRR abs/1802.09502 (2018) - 2016
- [i1]Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba:
End to End Learning for Self-Driving Cars. CoRR abs/1604.07316 (2016)
Coauthor Index
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