1 code implementation • 17 Feb 2024 • Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, PengFei Liu
As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation.
no code implementations • 8 Feb 2024 • Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie Chen
Despite remarkable advancements in emulating human-like behavior through Large Language Models (LLMs), current textual simulations do not adequately address the notion of time.
1 code implementation • 13 Jan 2024 • Yikai Zhang, Junlong Li, PengFei Liu
Large Language Models (LLMs) are known to have limited extrapolation ability beyond their pre-trained context window, constraining their application in downstream tasks with lengthy inputs.
no code implementations • 25 Sep 2023 • Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen
Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high.
2 code implementations • 17 Sep 2023 • Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xiao, Qianxi He, Xunzhe Zhou, Lida Chen, Xintao Wang, Yuncheng Huang, Haoning Ye, Zihan Li, Shisong Chen, Yikai Zhang, Zhouhong Gu, Jiaqing Liang, Yanghua Xiao
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
1 code implementation • 21 Jul 2023 • Jiachen Yao, Yikai Zhang, Songzhu Zheng, Mayank Goswami, Prateek Prasanna, Chao Chen
However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution.
1 code implementation • 13 Jun 2023 • Qianyu He, Yikai Zhang, Jiaqing Liang, Yuncheng Huang, Yanghua Xiao, Yunwen Chen
Similes play an imperative role in creative writing such as story and dialogue generation.
1 code implementation • NeurIPS 2023 • Saumya Gupta, Yikai Zhang, Xiaoling Hu, Prateek Prasanna, Chao Chen
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology.
1 code implementation • NeurIPS 2023 • Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, Junxian He
We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context.
1 code implementation • 12 May 2023 • Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka
We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
no code implementations • 2 Jan 2023 • Wenkang Zhu, Hui Li, Yikai Zhang, Yuqing Hou, Liwei Chen
Inference time of ViTSR and FCN was optimized to 50. 97 ms and 67. 86 ms on AI edge board after operator fusion and model pruning.
no code implementations • 6 Jun 2022 • Yikai Zhang, Jiachen Yao, Yusu Wang, Chao Chen
Topological loss based on persistent homology has shown promise in various applications.
no code implementations • 24 Mar 2022 • Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Mayank Goswami, Chao Chen, Dimitris Metaxas
Assuming data lies in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction, and the in-manifold adversarial risk due to perturbation within the manifold.
no code implementations • 29 Sep 2021 • Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Yuriy Nevmyvaka, Chao Chen
Learning and decision making in domains with naturally high noise-to-signal ratios – such as Finance or Public Health – can be challenging and yet extremely important.
no code implementations • NeurIPS 2021 • Songzhu Zheng, Yikai Zhang, Hubert Wagner, Mayank Goswami, Chao Chen
Deep neural networks are known to have security issues.
1 code implementation • ICLR 2021 • Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen
Label noise is frequently observed in real-world large-scale datasets.
Ranked #12 on Learning with noisy labels on ANIMAL
no code implementations • 10 Feb 2021 • Yikai Zhang, Wenjia Zhang, Sammy Bald, Vamsi Pingali, Chao Chen, Mayank Goswami
This raises the question: is the stability analysis of [18] tight for smooth functions, and if not, for what kind of loss functions and data distributions can the stability analysis be improved?
1 code implementation • 9 Feb 2021 • Yikai Zhang, Hui Qu, Qi Chang, Huidong Liu, Dimitris Metaxas, Chao Chen
A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.
no code implementations • 1 Jan 2021 • Yikai Zhang, Samuel Bald, Wenjia Zhang, Vamsi Pritham Pingali, Chao Chen, Mayank Goswami
We provide empirical evidence that this condition holds for several loss functions, and provide theoretical evidence that the known tight SGD stability bounds for convex and non-convex loss functions can be circumvented by HC loss functions, thus partially explaining the generalization of deep neural networks.
no code implementations • 15 Dec 2020 • Qi Chang, Zhennan Yan, Lohendran Baskaran, Hui Qu, Yikai Zhang, Tong Zhang, Shaoting Zhang, Dimitris N. Metaxas
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.
1 code implementation • ECCV 2020 • Hui Qu, Yikai Zhang, Qi Chang, Zhennan Yan, Chao Chen, Dimitris Metaxas
Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators?
1 code implementation • CVPR 2020 • Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, Dimitris Metaxas
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).