Zhijie Deng

According to our database, Zhijie Deng authored at least 35 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bayesian Exploration of Pre-trained Models for Low-shot Image Classification.

Yibo Miao, Yu Lei, Feng Zhou, Zhijie Deng

CoRR, 2024

SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation.

Hongjian Liu, Qingsong Xie, Zhijie Deng, Chen Chen, Shixiang Tang, Fueyang Fu, Zhengjun Zha, Haonan Lu

CoRR, 2024

CLLMs: Consistency Large Language Models.

Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, Hao Zhang

CoRR, 2024

2023

Heterogeneous multi-task Gaussian Cox processes.

Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu

Mach. Learn., December, 2023

Batch virtual adversarial training for graph convolutional networks.

Zhijie Deng, Yinpeng Dong, Jun Zhu

AI Open, January, 2023

Improved Operator Learning by Orthogonal Attention.

Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su

CoRR, 2023

BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference.

Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng

CoRR, 2023

LOVECon: Text-driven Training-Free Long Video Editing with ControlNet.

Zhenyi Liao, Zhijie Deng

CoRR, 2023

Online Speculative Decoding.

Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang

CoRR, 2023

Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks.

Hongcheng Gao, Hao Zhang, Yinpeng Dong, Zhijie Deng

CoRR, 2023

Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model.

Zhijie Deng, Hongcheng Gao, Yibo Miao, Hao Zhang

CoRR, 2023

Learning Sample Difficulty from Pre-trained Models for Reliable Prediction.

Peng Cui, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu

CoRR, 2023

On Calibrating Diffusion Probabilistic Models.

Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Accelerated Model Training via Bayesian Data Selection.

Zhijie Deng, Peng Cui, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Sample Difficulty from Pre-trained Models for Reliable Prediction.

Peng Cui, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation.

Zhijie Deng, Yucen Luo

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Efficient Inference for Dynamic Flexible Interactions of Neural Populations.

Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu

J. Mach. Learn. Res., 2022

Neural Eigenfunctions Are Structured Representation Learners.

Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu

CoRR, 2022

Deep Ensemble as a Gaussian Process Approximate Posterior.

Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu

CoRR, 2022

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning.

Zhijie Deng, Feng Zhou, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Confidence-based Reliable Learning under Dual Noises.

Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

NeuralEF: Deconstructing Kernels by Deep Neural Networks.

Zhijie Deng, Jiaxin Shi, Jun Zhu

Proceedings of the International Conference on Machine Learning, 2022

Exploring Memorization in Adversarial Training.

Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu

Proceedings of the Tenth International Conference on Learning Representations, 2022

BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning.

Zhijie Deng, Jun Zhu

Proceedings of the Asian Conference on Machine Learning, 2022

2021

Accurate and Reliable Forecasting using Stochastic Differential Equations.

Peng Cui, Zhijie Deng, Wenbo Hu, Jun Zhu

CoRR, 2021

Black-box Detection of Backdoor Attacks with Limited Information and Data.

Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

LiBRe: A Practical Bayesian Approach to Adversarial Detection.

Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning.

Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu

CoRR, 2020

Adversarial Distributional Training for Robust Deep Learning.

Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Understanding and Exploring the Network with Stochastic Architectures.

Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning.

Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures.

Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang

CoRR, 2019

Cluster Alignment With a Teacher for Unsupervised Domain Adaptation.

Zhijie Deng, Yucen Luo, Jun Zhu

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Cavs: An Efficient Runtime System for Dynamic Neural Networks.

Shizhen Xu, Hao Zhang, Graham Neubig, Wei Dai, Jin Kyu Kim, Zhijie Deng, Qirong Ho, Guangwen Yang, Eric P. Xing

Proceedings of the 2018 USENIX Annual Technical Conference, 2018

2017

Structured Generative Adversarial Networks.

Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017