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ORCIDZhijie Deng
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2020 – today
- 2024
[c25]Yibo Miao, Hongcheng Gao, Hao Zhang, Zhijie Deng:
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model. ACL (Findings) 2024: 6118-6130- [c24]Yibo Miao, Yu Lei, Feng Zhou, Zhijie Deng:
Bayesian Exploration of Pre-Trained Models for Low-Shot Image Classification. CVPR 2024: 23849-23859 - [c23]Zihao Zeng, Yibo Miao, Hongcheng Gao, Hao Zhang, Zhijie Deng:
AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models. EMNLP (Findings) 2024: 6223-6235 - [c22]Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng:
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference. ICLR 2024 - [c21]Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, Hao Zhang:
CLLMs: Consistency Large Language Models. ICML 2024 - [c20]Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang:
Online Speculative Decoding. ICML 2024 - [c19]Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su:
Improved Operator Learning by Orthogonal Attention. ICML 2024 - [c18]Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He:
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN. ICML 2024 - [i40]Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, Hao Zhang:
CLLMs: Consistency Large Language Models. CoRR abs/2403.00835 (2024) - [i39]Hongjian Liu, Qingsong Xie, Zhijie Deng, Chen Chen
, Shixiang Tang, Fueyang Fu, Zhengjun Zha, Haonan Lu:
SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation. CoRR abs/2403.01505 (2024) - [i38]Yibo Miao, Yu Lei, Feng Zhou, Zhijie Deng:
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification. CoRR abs/2404.00312 (2024) - [i37]Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He:
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN. CoRR abs/2406.03470 (2024) - [i36]Qingsong Xie, Zhenyi Liao, Chen Chen, Zhijie Deng, Shixiang Tang, Haonan Lu:
MLCM: Multistep Consistency Distillation of Latent Diffusion Model. CoRR abs/2406.05768 (2024) - [i35]Yuzi Yan, Yibo Miao, Jialian Li, Yipin Zhang, Jian Xie, Zhijie Deng, Dong Yan:
3D-Properties: Identifying Challenges in DPO and Charting a Path Forward. CoRR abs/2406.07327 (2024) - [i34]Zihao Zeng, Yibo Miao, Hongcheng Gao, Hao Zhang, Zhijie Deng:
AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models. CoRR abs/2406.13233 (2024) - [i33]Xiaoxuan Liu, Cade Daniel, Langxiang Hu, Woosuk Kwon, Zhuohan Li, Xiangxi Mo, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang:
Optimizing Speculative Decoding for Serving Large Language Models Using Goodput. CoRR abs/2406.14066 (2024) - [i32]Zhenyi Liao, Qingsong Xie, Chen Chen, Hannan Lu, Zhijie Deng:
Fine-tuning Diffusion Models for Enhancing Face Quality in Text-to-image Generation. CoRR abs/2406.17100 (2024) - [i31]Juntu Zhao, Junyu Deng, Yixin Ye, Chongxuan Li, Zhijie Deng, Dequan Wang:
Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models. CoRR abs/2408.00230 (2024) - [i30]Hongcheng Gao, Tianyu Pang, Chao Du, Taihang Hu, Zhijie Deng, Min Lin:
Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts. CoRR abs/2410.12777 (2024) - [i29]Zihao Zeng, Bokai Lin, Tianqi Hou, Hao Zhang, Zhijie Deng:
In-context KV-Cache Eviction for LLMs via Attention-Gate. CoRR abs/2410.12876 (2024) - [i28]Bokai Lin, Zihao Zeng, Zipeng Xiao, Siqi Kou, Tianqi Hou, Xiaofeng Gao, Hao Zhang, Zhijie Deng:
MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection. CoRR abs/2410.14731 (2024) - [i27]Yibo Miao, Bofei Gao, Shanghaoran Quan, Junyang Lin, Daoguang Zan, Jiaheng Liu, Jian Yang, Tianyu Liu, Zhijie Deng:
Aligning CodeLLMs with Direct Preference Optimization. CoRR abs/2410.18585 (2024) - 2023
- [j3]Zhijie Deng, Yinpeng Dong, Jun Zhu:
Batch virtual adversarial training for graph convolutional networks. AI Open 4: 73-79 (2023) - [j2]Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu:
Heterogeneous multi-task Gaussian Cox processes. Mach. Learn. 112(12): 5105-5134 (2023) - [c17]Zhijie Deng, Yucen Luo:
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation. ICCV 2023: 551-561 - [c16]Peng Cui, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu:
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction. NeurIPS 2023 - [c15]Zhijie Deng, Peng Cui, Jun Zhu:
Towards Accelerated Model Training via Bayesian Data Selection. NeurIPS 2023 - [c14]Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng:
On Calibrating Diffusion Probabilistic Models. NeurIPS 2023 - [i26]Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu:
Confidence-based Reliable Learning under Dual Noises. CoRR abs/2302.05098 (2023) - [i25]Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng:
On Calibrating Diffusion Probabilistic Models. CoRR abs/2302.10688 (2023) - [i24]Zhijie Deng, Yucen Luo:
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation. CoRR abs/2304.02841 (2023) - [i23]Peng Cui, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu:
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction. CoRR abs/2304.10127 (2023) - [i22]Zhijie Deng, Hongcheng Gao, Yibo Miao, Hao Zhang:
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model. CoRR abs/2305.16617 (2023) - [i21]Hongcheng Gao, Hao Zhang, Yinpeng Dong, Zhijie Deng:
Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks. CoRR abs/2306.13103 (2023) - [i20]Zhijie Deng, Peng Cui, Jun Zhu:
Towards Accelerated Model Training via Bayesian Data Selection. CoRR abs/2308.10544 (2023) - [i19]Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu:
Heterogeneous Multi-Task Gaussian Cox Processes. CoRR abs/2308.15364 (2023) - [i18]Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang:
Online Speculative Decoding. CoRR abs/2310.07177 (2023) - [i17]Zhenyi Liao, Zhijie Deng:
LOVECon: Text-driven Training-Free Long Video Editing with ControlNet. CoRR abs/2310.09711 (2023) - [i16]Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng:
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference. CoRR abs/2310.11142 (2023) - [i15]Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su:
Improved Operator Learning by Orthogonal Attention. CoRR abs/2310.12487 (2023) - 2022
- [j1]Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu:
Efficient Inference for Dynamic Flexible Interactions of Neural Populations. J. Mach. Learn. Res. 23: 211:1-211:49 (2022) - [c13]Zhijie Deng, Jun Zhu:
BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning. ACML 2022: 280-295 - [c12]Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu:
Exploring Memorization in Adversarial Training. ICLR 2022 - [c11]Zhijie Deng, Jiaxin Shi, Jun Zhu:
NeuralEF: Deconstructing Kernels by Deep Neural Networks. ICML 2022: 4976-4992 - [c10]Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu:
Confidence-based Reliable Learning under Dual Noises. NeurIPS 2022 - [c9]Zhijie Deng, Feng Zhou, Jun Zhu:
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. NeurIPS 2022 - [i14]Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu:
Deep Ensemble as a Gaussian Process Approximate Posterior. CoRR abs/2205.00163 (2022) - [i13]Zhijie Deng, Jiaxin Shi, Jun Zhu:
NeuralEF: Deconstructing Kernels by Deep Neural Networks. CoRR abs/2205.00165 (2022) - [i12]Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu:
Neural Eigenfunctions Are Structured Representation Learners. CoRR abs/2210.12637 (2022) - [i11]Zhijie Deng, Feng Zhou, Jun Zhu:
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. CoRR abs/2210.12642 (2022) - 2021
- [c8]Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu:
LiBRe: A Practical Bayesian Approach to Adversarial Detection. CVPR 2021: 972-982 - [c7]Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu:
Black-box Detection of Backdoor Attacks with Limited Information and Data. ICCV 2021: 16462-16471 - [i10]Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu:
Black-box Detection of Backdoor Attacks with Limited Information and Data. CoRR abs/2103.13127 (2021) - [i9]Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu:
LiBRe: A Practical Bayesian Approach to Adversarial Detection. CoRR abs/2103.14835 (2021) - [i8]Peng Cui, Zhijie Deng, Wenbo Hu, Jun Zhu:
Accurate and Reliable Forecasting using Stochastic Differential Equations. CoRR abs/2103.15041 (2021) - [i7]Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu:
Exploring Memorization in Adversarial Training. CoRR abs/2106.01606 (2021) - 2020
- [c6]Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing:
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning. NeurIPS 2020 - [c5]Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu:
Understanding and Exploring the Network with Stochastic Architectures. NeurIPS 2020 - [c4]Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su:
Adversarial Distributional Training for Robust Deep Learning. NeurIPS 2020 - [i6]Zhijie Deng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Adversarial Distributional Training for Robust Deep Learning. CoRR abs/2002.05999 (2020) - [i5]Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu:
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning. CoRR abs/2010.01979 (2020)
2010 – 2019
- 2019
- [c3]Zhijie Deng, Yucen Luo, Jun Zhu:
Cluster Alignment With a Teacher for Unsupervised Domain Adaptation. ICCV 2019: 9943-9952 - [i4]Zhijie Deng, Yinpeng Dong, Jun Zhu:
Batch Virtual Adversarial Training for Graph Convolutional Networks. CoRR abs/1902.09192 (2019) - [i3]Zhijie Deng, Yucen Luo, Jun Zhu:
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation. CoRR abs/1903.09980 (2019) - [i2]Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang:
DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures. CoRR abs/1911.09804 (2019) - 2018
- [c2]Shizhen Xu, Hao Zhang, Graham Neubig, Wei Dai, Jin Kyu Kim, Zhijie Deng, Qirong Ho, Guangwen Yang, Eric P. Xing:
Cavs: An Efficient Runtime System for Dynamic Neural Networks. USENIX ATC 2018: 937-950 - 2017
- [c1]Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. NIPS 2017: 3899-3909 - [i1]Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. CoRR abs/1711.00889 (2017)
Coauthor Index
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last updated on 2024-12-19 22:12 CET by the dblp team
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