Search Results for author: Ning Chen

Found 32 papers, 12 papers with code

Towards Integrated Fine-tuning and Inference when Generative AI meets Edge Intelligence

no code implementations5 Jan 2024 Ning Chen, Zhipeng Cheng, Xuwei Fan, Xiaoyu Xia, Lianfen Huang

The high-performance generative artificial intelligence (GAI) represents the latest evolution of computational intelligence, while the blessing of future 6G networks also makes edge intelligence (EI) full of development potential.

Improving Viewpoint Robustness for Visual Recognition via Adversarial Training

1 code implementation21 Jul 2023 Shouwei Ruan, Yinpeng Dong, Hang Su, Jianteng Peng, Ning Chen, Xingxing Wei

Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.

Towards Viewpoint-Invariant Visual Recognition via Adversarial Training

1 code implementation ICCV 2023 Shouwei Ruan, Yinpeng Dong, Hang Su, Jianteng Peng, Ning Chen, Xingxing Wei

Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object.

Automatic Speech Disentanglement for Voice Conversion using Rank Module and Speech Augmentation

no code implementations21 Jun 2023 Zhonghua Liu, Shijun Wang, Ning Chen

In this paper, we propose a VC model that can automatically disentangle speech into four components using only two augmentation functions, without the requirement of multiple hand-crafted features or laborious bottleneck tuning.

Disentanglement Voice Conversion

SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models

1 code implementation12 Apr 2023 Ning Chen, Jun Yue, Leyuan Fang, Shaobo Xia

The framework consists of a spectral-spatial diffusion module, and an attention-based classification module.

Classification Denoising +1

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

1 code implementation CVPR 2023 Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu

The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems.

Adversarial Robustness Face Recognition

High Speed Rotation Estimation with Dynamic Vision Sensors

no code implementations6 Sep 2022 Guangrong Zhao, Yiran Shen, Ning Chen, Pengfei Hu, Lei Liu, Hongkai Wen

By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets.

Vocal Bursts Intensity Prediction

Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets

no code implementations8 Aug 2022 Zhipeng Cheng, Xuwei Fan, Minghui LiWang, Ning Chen, Xianbin Wang

We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets.

Federated Learning reinforcement-learning +1

A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization

no code implementations20 Jun 2022 Ning Chen, Zhengke Sun, Tong Jia

In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization.

severity prediction

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

1 code implementation9 Jun 2022 Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu

Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation.

Continuous Control reinforcement-learning +2

Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges

no code implementations4 Jun 2022 Zhipeng Cheng, Xuwei Fan, Minghui LiWang, Ning Chen, Xiaoyu Xia, Xianbin Wang

The ever-growing concerns regarding data privacy have led to a paradigm shift in machine learning (ML) architectures from centralized to distributed approaches, giving rise to federated learning (FL) and split learning (SL) as the two predominant privacy-preserving ML mechanisms.

BIG-bench Machine Learning Federated Learning +1

One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer

1 code implementation20 Oct 2021 Yongquan Yang, Fengling Li, Yani Wei, Jie Chen, Ning Chen, Hong Bu

Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies.

BIG-bench Machine Learning Logical Reasoning

StackVAE-G: An efficient and interpretable model for time series anomaly detection

1 code implementation18 May 2021 Wenkai Li, WenBo Hu, Ting Chen, Ning Chen, Cheng Feng

We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels.

Anomaly Detection Graph Learning +2

The collider tests of a leptophilic scalar for the anomalous magnetic moments

no code implementations10 Feb 2021 Ning Chen, Bin Wang, Chang-Yuan Yao

Our results show that the leptophilic scalar in the mass range of $\mathcal{O}(10)- \mathcal{O}(1000 )\,\rm GeV$ can be fully probed by the future experimental searches at the HL-LHC and the lepton colliders at their early stages.

High Energy Physics - Phenomenology

A Survey on Ensemble Learning under the Era of Deep Learning

no code implementations21 Jan 2021 Yongquan Yang, Haijun Lv, Ning Chen

An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it.

Ensemble Learning

Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

2 code implementations ICLR 2020 Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu

Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e. g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.

Adversarial Robustness

Improving Adversarial Robustness via Promoting Ensemble Diversity

6 code implementations25 Jan 2019 Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu

Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks.

Adversarial Robustness

Transferable Adversarial Attacks for Image and Video Object Detection

2 code implementations30 Nov 2018 Xingxing Wei, Siyuan Liang, Ning Chen, Xiaochun Cao

Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection.

Generative Adversarial Network Object +2

Type-II 2HDM under the Precision Measurements at the $Z$-pole and a Higgs Factory

1 code implementation6 Aug 2018 Ning Chen, Tao Han, Shufang Su, Wei Su, Yongcheng Wu

We also find that the expected accuracies at the $Z$-pole and at a Higgs factory are quite complementary in constraining mass splittings of heavy Higgs bosons.

High Energy Physics - Phenomenology

Message Passing Stein Variational Gradient Descent

no code implementations ICML 2018 Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.

Bayesian Inference Variational Inference

A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection

no code implementations23 Sep 2017 Jorge Rivero, Bernardete Ribeiro, Ning Chen, Fátima Silva Leite

This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Learning and the Inference Stage.

Attribute Network Intrusion Detection +1

SAM: Semantic Attribute Modulation for Language Modeling and Style Variation

no code implementations1 Jul 2017 Wenbo Hu, Lifeng Hua, Lei LI, Hang Su, Tian Wang, Ning Chen, Bo Zhang

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation.

Attribute Language Modelling

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

no code implementations7 Dec 2015 Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features.

Bayesian Inference Data Augmentation +1

Dropout Training for SVMs with Data Augmentation

no code implementations10 Aug 2015 Ning Chen, Jun Zhu, Jianfei Chen, Ting Chen

Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.

Data Augmentation Representation Learning

Dropout Training for Support Vector Machines

no code implementations16 Apr 2014 Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang

To deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques.

Data Augmentation

Gibbs Max-margin Topic Models with Data Augmentation

no code implementations10 Oct 2013 Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang

Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule.

Data Augmentation General Classification +3

Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs

no code implementations5 Oct 2012 Jun Zhu, Ning Chen, Eric P. Xing

When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes.

Bayesian Inference Multi-Task Learning

On the Complexity of Trial and Error

no code implementations6 May 2012 Xiaohui Bei, Ning Chen, Shengyu Zhang

On one hand, despite the seemingly very little information provided by the verification oracle, efficient algorithms do exist for a number of important problems.

Infinite Latent SVM for Classification and Multi-task Learning

no code implementations NeurIPS 2011 Jun Zhu, Ning Chen, Eric P. Xing

Unlike existing nonparametric Bayesian models, which rely solely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations, we study nonparametric Bayesian inference with regularization on the desired posterior distributions.

Bayesian Inference Classification +2

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