Zhijie Deng | DeepAI

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239 publications
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226 publications
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∙ 08/29/2023

Heterogeneous Multi-Task Gaussian Cox Processes

This paper presents a novel extension of multi-task Gaussian Cox process...

0 Feng Zhou, et al. ∙

∙ 08/21/2023

Towards Accelerated Model Training via Bayesian Data Selection

Mislabeled, duplicated, or biased data in real-world scenarios can lead ...

0 Zhijie Deng, et al. ∙

∙ 04/20/2023

Learning Sample Difficulty from Pre-trained Models for Reliable Prediction

Large-scale pre-trained models have achieved remarkable success in a var...

0 Peng Cui, et al. ∙

∙ 04/06/2023

Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation

Unsupervised semantic segmentation is a long-standing challenge in compu...

0 Zhijie Deng, et al. ∙

∙ 02/21/2023

On Calibrating Diffusion Probabilistic Models

Recently, diffusion probabilistic models (DPMs) have achieved promising ...

0 Tianyu Pang, et al. ∙

∙ 02/10/2023

Confidence-based Reliable Learning under Dual Noises

Deep neural networks (DNNs) have achieved remarkable success in a variet...

0 Peng Cui, et al. ∙

∙ 10/23/2022

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

Laplace approximation (LA) and its linearized variant (LLA) enable effor...

0 Zhijie Deng, et al. ∙

∙ 04/30/2022

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Learning the principal eigenfunctions of an integral operator defined by...

0 Zhijie Deng, et al. ∙

∙ 04/30/2022

Deep Ensemble as a Gaussian Process Approximate Posterior

Deep Ensemble (DE) is an effective alternative to Bayesian neural networ...

0 Zhijie Deng, et al. ∙

∙ 06/03/2021

Exploring Memorization in Adversarial Training

It is well known that deep learning models have a propensity for fitting...

0 Yinpeng Dong, et al. ∙

∙ 03/28/2021

Accurate and Reliable Forecasting using Stochastic Differential Equations

It is critical yet challenging for deep learning models to properly char...

0 Peng Cui, et al. ∙

∙ 03/27/2021

LiBRe: A Practical Bayesian Approach to Adversarial Detection

Despite their appealing flexibility, deep neural networks (DNNs) are vul...

0 Zhijie Deng, et al. ∙

∙ 03/24/2021

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

Although deep neural networks (DNNs) have made rapid progress in recent ...

0 Yinpeng Dong, et al. ∙

∙ 02/14/2020

Adversarial Distributional Training for Robust Deep Learning

Adversarial training (AT) is among the most effective techniques to impr...

0 Zhijie Deng, et al. ∙

∙ 11/22/2019

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

Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...

13 Zhijie Deng, et al. ∙

∙ 03/24/2019

Cluster Alignment with a Teacher for Unsupervised Domain Adaptation

Deep learning methods have shown promise in unsupervised domain adaptati...

0 Zhijie Deng, et al. ∙

∙ 02/25/2019

Batch Virtual Adversarial Training for Graph Convolutional Networks

We present batch virtual adversarial training (BVAT), a novel regulariza...

0 Zhijie Deng, et al. ∙

∙ 11/02/2017

Structured Generative Adversarial Networks

We study the problem of conditional generative modeling based on designa...

0 Zhijie Deng, et al. ∙

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