no code implementations • 28 Feb 2024 • Zhuofeng Wu, Yusuke Monno, Masatoshi Okutomi
In this paper, we address the task of aberration-aware depth-from-defocus (DfD), which takes account of spatially variant point spread functions (PSFs) of a real camera.
no code implementations • 22 Feb 2024 • Zhuofeng Wu, He Bai, Aonan Zhang, Jiatao Gu, VG Vinod Vydiswaran, Navdeep Jaitly, Yizhe Zhang
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first.
no code implementations • 15 Oct 2023 • Zhuofeng Wu, Chaowei Xiao, VG Vinod Vydiswaran
In this paper, we propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness.
1 code implementation • NeurIPS 2023 • Yizhe Zhang, Jiatao Gu, Zhuofeng Wu, Shuangfei Zhai, Josh Susskind, Navdeep Jaitly
Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation.
no code implementations • 24 May 2023 • Jiongxiao Wang, Zichen Liu, Keun Hee Park, Zhuojun Jiang, Zhaoheng Zheng, Zhuofeng Wu, Muhao Chen, Chaowei Xiao
We propose a novel attack method named advICL, which aims to manipulate only the demonstration without changing the input to mislead the models.
1 code implementation • 3 May 2023 • Jiazhao Li, Zhuofeng Wu, Wei Ping, Chaowei Xiao, V. G. Vinod Vydiswaran
Textual backdoor attack, as a novel attack model, has been shown to be effective in adding a backdoor to the model during training.
no code implementations • 27 Apr 2023 • Jiazhao Li, Yijin Yang, Zhuofeng Wu, V. G. Vinod Vydiswaran, Chaowei Xiao
Textual backdoor attacks pose a practical threat to existing systems, as they can compromise the model by inserting imperceptible triggers into inputs and manipulating labels in the training dataset.
no code implementations • NAACL 2022 • Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V. G. Vinod Vydiswaran, Hao Ma
Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.
no code implementations • 31 Dec 2020 • Zhuofeng Wu, Sinong Wang, Jiatao Gu, Madian Khabsa, Fei Sun, Hao Ma
Pre-trained language models have proven their unique powers in capturing implicit language features.
Ranked #5 on Question Answering on Quora Question Pairs