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### 2024
{{< publication title="SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN" venue="ICML 2024" paperLink="" codeLink="" award="" project="" data-topic="Scalable ML" >}}
Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He
{{< /publication >}}
{{< publication title="CLLMs: Consistency Large Language Models" venue="ICML 2024" paperLink="https://arxiv.org/pdf/2403.00835.pdf" codeLink="https://github.com/hao-ai-lab/Consistency_LLM" award="" project="" data-topic="Selected, Large Language Models, Scalable ML, ML Systems" >}}
Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng†, Hao Zhang
{{< /publication >}}
{{< publication title="Improved Operator Learning by Orthogonal Attention" venue="ICML 2024" paperLink="https://arxiv.org/pdf/2310.12487.pdf" codeLink="" award="" project="" data-topic="Deep Spectral Methods, Neural Eigenfunctions, AI4PDE" >}}
Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng†, Hang Su†
{{< /publication >}}
{{< publication title="Online Speculative Decoding" venue="ICML 2024" paperLink="https://arxiv.org/pdf/2310.07177.pdf" codeLink="" award="" project="" data-topic="Large Language Models, ML Systems, Scalable ML" >}}
Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng†, Alvin Cheung, Hao Zhang†
{{< /publication >}}
{{< publication title="LOVECon: Text-driven Training-Free Long Video Editing with ControlNet" venue="AI for Content Creation Workshop @ CVPR 2024" paperLink="https://arxiv.org/pdf/2310.09711.pdf" codeLink="" award="" project="" data-topic="Generative Models, Diffusion Models" >}}
Zhenyi Liao, Zhijie Deng†
{{< /publication >}}
{{< publication title="Bayesian Exploration of Pre-trained Models for Low-shot Image Classification" venue="CVPR 2024" paperLink="https://arxiv.org/pdf/2404.00312.pdf" codeLink="" award="" project="" data-topic="Bayesian Deep Learning, Uncertainty Quantification, Bayesian Inference" >}}
Yibo Miao, Yu Lei, Feng Zhou†, Zhijie Deng†
{{< /publication >}}
{{< publication title="BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference" venue="ICLR 2024" paperLink="https://arxiv.org/pdf/2310.11142.pdf" codeLink="" award="" project="" data-topic="Generative Models, Bayesian Deep Learning" >}}
Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li†, Zhijie Deng†
{{< /publication >}}
 
### 2023
{{< publication title="Towards Accelerated Model Training via Bayesian Data Selection" venue="NeurIPS 2023" paperLink="https://arxiv.org/pdf/2308.10544.pdf" codeLink="" award="" project="" data-topic="Data Selection, Bayesian Deep Learning" >}}
Zhijie Deng*, Peng Cui*, Jun Zhu
{{< /publication >}}
{{< publication title="Learning Sample Difficulty from Pre-trained Models for Reliable Prediction" venue="NeurIPS 2023" paperLink="https://arxiv.org/pdf/2304.10127.pdf" codeLink="" award="" project="" data-topic="Uncertainty Quantification, Sample Difficulty Quantification, Data-efficient Learning" >}}
Peng Cui, Dan Zhang, Zhijie Deng†, Yinpeng Dong, Jun Zhu†
{{< /publication >}}
{{< publication title="On Calibrating Diffusion Probabilistic Models" venue="NeurIPS 2023" paperLink="https://arxiv.org/pdf/2302.10688.pdf" codeLink="" award="" project="" data-topic="Generative Models, Diffusion Models" >}}
Tianyu Pang†, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng†
{{< /publication >}}
{{< publication title="Heterogeneous Multi-Task Gaussian Cox Processes" venue="Machine Learning 2023" paperLink="https://link.springer.com/article/10.1007/s10994-023-06382-1" codeLink="" award="" project="" data-topic="Bayesian Inference" >}}
Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu
{{< /publication >}}
{{< publication title="Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation" venue="ICCV 2023" paperLink="https://openaccess.thecvf.com/content/ICCV2023/papers/Deng_Learning_Neural_Eigenfunctions_for_Unsupervised_Semantic_Segmentation_ICCV_2023_paper.pdf" codeLink="https://github.com/thudzj/NeuralEigenfunctionSegmentor" award="" project="" data-topic="Deep Spectral Methods, Neural Eigenfunctions" >}}
Zhijie Deng, Yucen Luo
{{< /publication >}}
{{< publication title="Batch Virtual Adversarial Training for Graph Convolutional Networks" venue="AI Open 2023" paperLink="https://www.sciencedirect.com/science/article/pii/S2666651023000098" codeLink="https://github.com/thudzj/bvat" award="" project="" data-topic="Data-efficient Learning, Graph Neural Networks" >}}
Zhijie Deng, Yinpeng Dong, Jun Zhu
{{< /publication >}}
 
### 2022
{{< publication title="BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning" venue="ACML 2022" paperLink="https://proceedings.mlr.press/v189/deng23b/deng23b.pdf" codeLink="https://github.com/thudzj/ScalableBDL" award="" project="" data-topic="Bayesian Deep Learning, Uncertainty Quantification, Bayesian Inference" >}}
Zhijie Deng, Jun Zhu
{{< /publication >}}
{{< publication title="Accelerated Linearized Laplace Approximation for Bayesian Deep Learning" venue="NeurIPS 2022" paperLink="https://openreview.net/pdf?id=jftNpltMgz" codeLink="https://github.com/thudzj/ELLA" award="Spotlight" project="" data-topic="Bayesian Deep Learning, Uncertainty Quantification, Bayesian Inference" >}}
Zhijie Deng, Feng Zhou, Jun Zhu
{{< /publication >}}
{{< publication title="Confidence-based Reliable Learning under Dual Noises" venue="NeurIPS 2022" paperLink="https://openreview.net/pdf?id=7fGIR2oIHTl" codeLink="" award="" project="" data-topic="Uncertainty Quantification, Data-efficient Learning" >}}
Peng Cui, Yang Yue, Zhijie Deng†, Jun Zhu†
{{< /publication >}}
{{< publication title="Efficient Inference for Dynamic Flexible Interactions of Neural Populations" venue="JMLR 2022" paperLink="https://www.jmlr.org/papers/volume23/21-1273/21-1273.pdf" codeLink="" award="" project="" data-topic="Bayesian Inference" >}}
Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu
{{< /publication >}}
{{< publication title="NeuralEF: Deconstructing Kernels by Deep Neural Networks" venue="ICML 2022" paperLink="https://proceedings.mlr.press/v162/deng22b/deng22b.pdf" codeLink="" award="" project="" data-topic="Deep Spectral Methods, Kernel Approximation, Neural Eigenfunctions" >}}
Zhijie Deng, Jiaxin Shi, Jun Zhu
{{< /publication >}}
{{< publication title="Exploring Memorization in Adversarial Training" venue="ICLR 2022" paperLink="https://openreview.net/forum?id=7gE9V9GBZaI" codeLink="" award="" project="" data-topic="Adversarial Defense" >}}
Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu
{{< /publication >}}
 
### 2021
{{< publication title="Black-box Detection of Backdoor Attacks with Limited Information and Data" venue="ICCV 2021" paperLink="https://arxiv.org/pdf/2103.13127.pdf" codeLink="" award="" project="" data-topic="Adversarial Defense" >}}
Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu
{{< /publication >}}
{{< publication title="LiBRe: A Practical Bayesian Approach to Adversarial Detection" venue="CVPR 2021" paperLink="https://arxiv.org/pdf/2103.14835.pdf" codeLink="https://github.com/thudzj/ScalableBDL/tree/efficient/exps" award="Valse 2021 Spotlight, 2021.10" project="" data-topic="Bayesian Deep Learning, Adversarial Defense" >}}
Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu
{{< /publication >}}
{{< publication title="Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure" venue="2nd Workshop on Neural Architecture Search at ICLR 2021" paperLink="https://arxiv.org/pdf/1911.09804.pdf" codeLink="" award="" project="" data-topic="Bayesian Deep Learning, Neural Architecture Search" >}}
Zhijie Deng, Yucen Luo, Jun Zhu
{{< /publication >}}
 
### 2020
{{< publication title="Adversarial Distributional Training for Robust Deep Learning" venue="NeurIPS 2020" paperLink="https://papers.nips.cc/paper/2020/file/5de8a36008b04a6167761fa19b61aa6c-Paper.pdf" codeLink="https://github.com/thudzj/adt" award="" project="" data-topic="Adversarial Defense, Generative Models" >}}
Yinpeng Dong*, Zhijie Deng*, Tianyu Pang, Hang Su, Jun Zhu
{{< /publication >}}
{{< publication title="Understanding and Exploring the Network with Stochastic Architectures" venue="NeurIPS 2020" paperLink="https://papers.nips.cc/paper/2020/file/aa85e45da94cb0d78853c50ba636a15a-Paper.pdf" codeLink="https://github.com/thudzj/nsa" award="" project="" data-topic="Bayesian Deep Learning, Neural Architecture Search" >}}
Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu
{{< /publication >}}
{{< publication title="Autosync: Learning to Synchronize for Data-parallel Distributed Deep Learning" venue="NeurIPS 2020" paperLink="https://proceedings.neurips.cc/paper/2020/file/0a2298a72858d90d5c4b4fee954b6896-Paper.pdf" codeLink="https://github.com/petuum/autodist" award="" project="" data-topic="Selected, ML Systems, Scalable ML, AutoML" >}}
Hao Zhang*, Yuan Li*, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric Xing
{{< /publication >}}
 
### 2019
{{< publication title="Cluster Alignment with a Teacher for Unsupervised Domain Adaptation" venue="ICCV 2019" paperLink="http://openaccess.thecvf.com/content_ICCV_2019/papers/Deng_Cluster_Alignment_With_a_Teacher_for_Unsupervised_Domain_Adaptation_ICCV_2019_paper.pdf" codeLink="https://github.com/thudzj/cat" award="" project="" data-topic="Selected, Generative Models" >}}
Zhijie Deng, Yucen Luo, Jun Zhu
{{< /publication >}}
 
### 2018
{{< publication title="Cavs: An Efficient Runtime System for Dynamic Neural Networks" venue="ATC 2018" paperLink="https://www.usenix.org/system/files/conference/atc18/atc18-xu-shizhen.pdf" codeLink="https://github.com/zhisbug/Cavs" award="" project="" data-topic="Selected, ML Systems" >}}
Shizhen Xu*, Hao Zhang*, Graham Neubig, Wei Dai, Jin Kyu Kim, Zhijie Deng, Qirong Ho, Guangwen Yang, Eric P Xing
{{< /publication >}}
 
### 2017
{{< publication title="Structured Generative Adversarial Networks" venue="NeurIPS 2017" paperLink="https://proceedings.neurips.cc/paper/2017/file/c3535febaff29fcb7c0d20cbe94391c7-Paper.pdf" codeLink="https://github.com/thudzj/StructuredGAN" award="Nvidia Pioneer Research Award" project="" data-topic="Generative Models" >}}
Zhijie Deng*, Hao Zhang*, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P Xing
{{< /publication >}}