Zhijie Deng

发表

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

Jun Zhu, Yucen Luo, Zhijie Deng, 2019, ArXiv.

Structured Generative Adversarial Networks 1

E. Xing, Shizhen Xu, Xiaodan Liang, 2017 .

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Jun Zhu, Zhijie Deng, Jiaxin Shi, 2022, ArXiv.

Deep Ensemble as a Gaussian Process Approximate Posterior

Jun Zhu, Feng Zhou, Zhijie Deng, 2022, ArXiv.

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Jun Zhu, Zhijie Deng, Jiaxin Shi, 2022, ArXiv.

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Jun Zhu, Zhijie Deng, Jiaxin Shi, 2022, ArXiv.

Neural Eigenfunctions Are Structured Representation Learners

Cewu Lu, Zhijie Deng, Hao Zhang, 2022, ArXiv.

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Jun Zhu, Zhijie Deng, Jiaxin Shi, 2022, ICML.

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

Feng Zhou, Jun Zhu, Zhijie Deng, 2022, NeurIPS.

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

Jun Zhu, Zhijie Deng, 2020, ACML.

Exploring Memorization in Adversarial Training

Jun Zhu, Hang Su, Tianyu Pang, 2021, International Conference on Learning Representations.

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

Hao Zhang, Eric P. Xing, Lawrence Carin, 2020, NeurIPS.

Cluster Alignment With a Teacher for Unsupervised Domain Adaptation

Jun Zhu, Zhijie Deng, Yucen Luo, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Batch Virtual Adversarial Training for Graph Convolutional Networks pdf

Jun Zhu, Yinpeng Dong, Zhijie Deng, 2019, AI Open.

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

Jun Zhu, Zihao Xiao, Yinpeng Dong, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

LiBRe: A Practical Bayesian Approach to Adversarial Detection

Jun Zhu, Zhijie Deng, Shizhen Xu, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Adversarial Distributional Training for Robust Deep Learning pdf

Hang Su, Yinpeng Dong, Zhijie Deng, 2020, NeurIPS.

Learning Sample Difficulty from Pre-trained Models for Reliable Prediction

Zhijie Deng, Yinpeng Dong, Junyi Zhu, 2023, ArXiv.

On Calibrating Diffusion Probabilistic Models

Min Lin, Chao Du, Tianyu Pang, 2023, NeurIPS.

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

Hao Zhang, Zhijie Deng, Yinpeng Dong, 2023, ArXiv.

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

Hao Zhang, Zhijie Deng, Hongcheng Gao, 2023, ArXiv.

Cavs: An Efficient Runtime System for Dynamic Neural Networks

E. Xing, Qirong Ho, Jin Kyu Kim, 2018, USENIX Annual Technical Conference.

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

Hao Zhang, Jun Zhu, Zhijie Deng, 2020, ArXiv.

Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation

Yucen Luo, Zhijie Deng, 2023, arXiv.org.

Heterogeneous multi-task Gaussian Cox processes

Feng Zhou, Zhijie Deng, Fengxiang He, 2023, Machine Learning.

Improved Operator Learning by Orthogonal Attention

Zhijie Deng, Zhongkai Hao, Zipeng Xiao, 2023, ArXiv.

Neural Eigenfunctions Are Structured Representation Learners

Cewu Lu, Zhijie Deng, Hao Zhang, 2022, ArXiv.

Understanding and Exploring the Network with Stochastic Architectures

Shifeng Zhang, Yinpeng Dong, Zhijie Deng, 2020, NeurIPS.

Towards Accelerated Model Training via Bayesian Data Selection

Jun Zhu, Zhijie Deng, Peng Cui, 2023, NeurIPS.