Search Results for author: Zhijie Deng

Found 28 papers, 15 papers with code

Improved Operator Learning by Orthogonal Attention

1 code implementation19 Oct 2023 Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning.

Operator learning

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

no code implementations17 Oct 2023 Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng

In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference.

Bayesian Inference Image Generation

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

1 code implementation15 Oct 2023 Zhenyi Liao, Zhijie Deng

Leveraging pre-trained conditional diffusion models for video editing without further tuning has gained increasing attention due to its promise in film production, advertising, etc.

Style Transfer Video Editing +1

Online Speculative Decoding

no code implementations11 Oct 2023 Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, Hao Zhang

We develop a prototype of online speculative decoding based on online knowledge distillation and evaluate it using both synthetic and real query data on several popular LLMs.

Knowledge Distillation

Heterogeneous Multi-Task Gaussian Cox Processes

1 code implementation29 Aug 2023 Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu

This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e. g., classification and regression, via multi-output Gaussian processes (MOGP).

Bayesian Inference Data Augmentation +2

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

no code implementations16 Jun 2023 Hongcheng Gao, Hao Zhang, Yinpeng Dong, Zhijie Deng

Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions.

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

no code implementations26 May 2023 Zhijie Deng, Hongcheng Gao, Yibo Miao, Hao Zhang

The detection of machine-generated text, especially from large language models (LLMs), is crucial in preventing serious social problems resulting from their misuse.

Confidence-based Reliable Learning under Dual Noises

no code implementations10 Feb 2023 Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu

Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization.

Model Optimization

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

1 code implementation23 Oct 2022 Zhijie Deng, Feng Zhou, Jun Zhu

Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks.

Neural Eigenfunctions Are Structured Representation Learners

1 code implementation23 Oct 2022 Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu

Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparametric manner, Neural Eigenmap leverages NeuralEF to parametrically model eigenfunctions using a neural network.

Contrastive Learning Data Augmentation +7

NeuralEF: Deconstructing Kernels by Deep Neural Networks

1 code implementation30 Apr 2022 Zhijie Deng, Jiaxin Shi, Jun Zhu

Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems.

Image Classification

Deep Ensemble as a Gaussian Process Approximate Posterior

no code implementations30 Apr 2022 Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu

In this way, we relate DE to Bayesian inference to enjoy reliable Bayesian uncertainty.

Bayesian Inference

Deep Ensemble as a Gaussian Process Posterior

no code implementations29 Sep 2021 Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu

Deep Ensemble (DE) is a flexible, feasible, and effective alternative to Bayesian neural networks (BNNs) for uncertainty estimation in deep learning.

Variational Inference

Exploring Memorization in Adversarial Training

1 code implementation ICLR 2022 Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu

In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, and especially robust overfitting of the adversarially trained models.


Accurate and Reliable Forecasting using Stochastic Differential Equations

no code implementations28 Mar 2021 Peng Cui, Zhijie Deng, WenBo Hu, Jun Zhu

It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments.

Prediction Intervals

LiBRe: A Practical Bayesian Approach to Adversarial Detection

1 code implementation CVPR 2021 Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.

Adversarial Defense

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

no code implementations ICCV 2021 Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu

Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.

Understanding and Exploring the Network with Stochastic Architectures

1 code implementation NeurIPS 2020 Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu

In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem.

Neural Architecture Search

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

1 code implementation5 Oct 2020 Zhijie Deng, Jun Zhu

Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibility, and reliability.

Variational Inference

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

no code implementations28 Sep 2020 Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu

Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates.

Variational Inference

Adversarial Distributional Training for Robust Deep Learning

1 code implementation NeurIPS 2020 Yinpeng Dong, Zhijie Deng, Tianyu Pang, Hang Su, Jun Zhu

Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples.


Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure

1 code implementation22 Nov 2019 Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang

Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights.

Bayesian Inference Neural Architecture Search +1

Deep Bayesian Structure Networks

1 code implementation25 Sep 2019 Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang

Bayesian neural networks (BNNs) introduce uncertainty estimation to deep networks by performing Bayesian inference on network weights.

Bayesian Inference Neural Architecture Search +1

Cluster Alignment with a Teacher for Unsupervised Domain Adaptation

1 code implementation ICCV 2019 Zhijie Deng, Yucen Luo, Jun Zhu

Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution.

Clustering Unsupervised Domain Adaptation

Batch Virtual Adversarial Training for Graph Convolutional Networks

no code implementations25 Feb 2019 Zhijie Deng, Yinpeng Dong, Jun Zhu

We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs).

General Classification Node Classification

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