End to End Learning for Self-Driving Cars. - 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; et al. (CoRR, 2016)

Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples. - Jake Zhao; Kyunghyun Cho (CoRR, 2018)

Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples. - Jake Zhao; Kyunghyun Cho (CVPR, 2019)

Levenshtein Transformer. - Jiatao Gu; Changhan Wang; Jake Zhao (CoRR, 2019)

Interactive Rainbow Score: A Visual-centered Multimodal Flute Tutoring System. - Daniel Chin; Yian Zhang; Tianyu Zhang; Jake Zhao; Gus Xia (CoRR, 2020)

A critical look at the current train/test split in machine learning. - Jimin Tan; Jianan Yang; Sai Wu; Gang Chen 001; Jake Zhao (CoRR, 2021)

Joining datasets via data augmentation in the label space for neural networks. - Jake Zhao; Mingfeng Ou; Linji Xue; Yunkai Cui; Sai Wu; Gang Chen 001 (CoRR, 2021)

Distill The Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation. - Ru Peng; Yawen Zeng; Jake Zhao (EMNLP, 2022)

Benchmarking Domain Generalization on EEG-based Emotion Recognition. - Yan Li 002; Hao Chen 081; Jake Zhao; Haolan Zhang 001; Jinpeng Li 002 (CoRR, 2022)

Fast Adaptation via Prompted Data: An Efficient Cross-Domain Fine-tuning Method for Large Language Models. - Yiming Zhang 023; Hantao Yang; Haobo Wang 001; Jake Zhao (LREC/COLING, 2024)