Search Results for author: Lan Zhang

Found 31 papers, 9 papers with code

On the Effect of Isotropy on VAE Representations of Text

1 code implementation ACL 2022 Lan Zhang, Wray Buntine, Ehsan Shareghi

Injecting desired geometric properties into text representations has attracted a lot of attention.

The Effectiveness of Large Language Models (ChatGPT and CodeBERT) for Security-Oriented Code Analysis

1 code implementation24 Jul 2023 Zhilong Wang, Lan Zhang, Chen Cao, Peng Liu

However, we observed that the strengths and limitations of adopting these LLMs to the code analysis have not been investigated.

Code Generation Language Modelling +1

Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

no code implementations10 Jul 2023 Gaurav Bagwe, Xiaoyong Yuan, Miao Pan, Lan Zhang

Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients.

Continual Learning

Tight Memory-Regret Lower Bounds for Streaming Bandits

no code implementations13 Jun 2023 Shaoang Li, Lan Zhang, Junhao Wang, Xiang-Yang Li

We establish the tight worst-case regret lower bound of $\Omega \left( (TB)^{\alpha} K^{1-\alpha}\right), \alpha = 2^{B} / (2^{B+1}-1)$ for any algorithm with a time horizon $T$, number of arms $K$, and number of passes $B$.

FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning

no code implementations21 Feb 2023 Anran Li, Hongyi Peng, Lan Zhang, Jiahui Huang, Qing Guo, Han Yu, Yang Liu

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model.

Feature Importance feature selection +1

Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning

no code implementations20 Jan 2023 Lan Zhang, Chen Cao, Zhilong Wang, Peng Liu

The Bidirectional Encoder Representations from Transformers (BERT) were proposed in the natural language process (NLP) and shows promising results.

Representation Learning

Distributed Pruning Towards Tiny Neural Networks in Federated Learning

no code implementations5 Dec 2022 Hong Huang, Lan Zhang, Chaoyue Sun, Ruogu Fang, Xiaoyong Yuan, Dapeng Wu

To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices.

Federated Learning Network Pruning

MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

1 code implementation28 Sep 2022 Mu Yuan, Lan Zhang, Zimu Zheng, Yi-Nan Zhang, Xiang-Yang Li

The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices.

Collaborative Inference Multi-Task Learning +1

InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference

1 code implementation28 Sep 2022 Mu Yuan, Lan Zhang, Fengxiang He, Xueting Tong, Miao-Hui Song, Zhengyuan Xu, Xiang-Yang Li

Previous efforts have tailored effective solutions for many applications, but left two essential questions unanswered: (1) theoretical filterability of an inference workload to guide the application of input filtering techniques, thereby avoiding the trial-and-error cost for resource-constrained mobile applications; (2) robust discriminability of feature embedding to allow input filtering to be widely effective for diverse inference tasks and input content.

Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning

no code implementations21 Jul 2022 Gaurav Bagwe, Jian Li, Xiaoyong Yuan, Lan Zhang

Moreover, to improve data efficiency and provide better generalization performance, we train the policy model with augmented data (e. g., noisy BSM and noisy surveillance images).

Autonomous Driving reinforcement-learning +1

Topology-aware Generalization of Decentralized SGD

1 code implementation25 Jun 2022 Tongtian Zhu, Fengxiang He, Lan Zhang, Zhengyang Niu, Mingli Song, DaCheng Tao

Our theory indicates that the generalizability of D-SGD is positively correlated with the spectral gap, and can explain why consensus control in initial training phase can ensure better generalization.

Residue-based Label Protection Mechanisms in Vertical Logistic Regression

no code implementations9 May 2022 Juntao Tan, Lan Zhang, Yang Liu, Anran Li, Ye Wu

To deal with this, we then propose three protection mechanisms, e. g., additive noise mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages local differential privacy and homomorphic encryption techniques, to prevent the attack and improve the robustness of the vertical logistic regression.

Federated Learning Inference Attack +1

Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection

no code implementations22 Apr 2022 Saket S. Chaturvedi, Lan Zhang, Xiaoyong Yuan

Specifically, GLA integrates an early-stage fusion via a local attention network and a late-stage fusion via a global attention network to deal with both local and global information, which automatically allocates higher weights to the modality with better detection features at the late-stage fusion to cope with the specific weather condition adaptively.

object-detection Object Detection

Membership Inference Attacks and Defenses in Neural Network Pruning

1 code implementation7 Feb 2022 Xiaoyong Yuan, Lan Zhang

We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members.

Inference Attack Membership Inference Attack +2

The Neglected Sibling: Isotropic Gaussian Posterior for VAE

1 code implementation14 Oct 2021 Lan Zhang, Wray Buntine, Ehsan Shareghi

Deep generative models have been widely used in several areas of NLP, and various techniques have been proposed to augment them or address their training challenges.

FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models

no code implementations8 Sep 2021 Lan Zhang, Dapeng Wu, Xiaoyong Yuan

To achieve knowledge transfer across these heterogeneous on-device models, a zero-shot distillation approach is designed without any prerequisites for private on-device data, which is contrary to certain prior research based on a public dataset or a pre-trained data generator.

Federated Learning Transfer Learning

A Vertical Federated Learning Framework for Horizontally Partitioned Labels

no code implementations18 Jun 2021 Wensheng Xia, Ying Li, Lan Zhang, Zhonghai Wu, Xiaoyong Yuan

To address these challenges, we propose a novel vertical federated learning framework named Cascade Vertical Federated Learning (CVFL) to fully utilize all horizontally partitioned labels to train neural networks with privacy-preservation.

Federated Learning

Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets

1 code implementation ACL (RepL4NLP) 2021 Lan Zhang, Victor Prokhorov, Ehsan Shareghi

To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain.

Disentanglement Inductive Bias

Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach

no code implementations18 Mar 2021 Xianhao Chen, Guangyu Zhu, Lan Zhang, Yuguang Fang, Linke Guo, Xinguang Chen

As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19.

Exploring the Galactic Anticenter substructure with LAMOST & Gaia DR2

no code implementations7 Jan 2021 Jing Li, Xiang-Xiang Xue, Chao Liu, Bo Zhang, Hans-Walter Rix, Jeffrey L. Carlin, Chengqun Yang, Rene A. Mendez, Jing Zhong, Hao Tian, Lan Zhang, Yan Xu, Yaqian Wu, Gang Zhao, Ruixiang Chang

Their location in [$\alpha$/M] vs. [M/H] space is more metal poor than typical thin disk stars, with [$\alpha$/M] \textbf{lower} than the thick disk.

Astrophysics of Galaxies

ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles

no code implementations21 Sep 2020 Xiaoyong Yuan, Leah Ding, Lan Zhang, Xiaolin Li, Dapeng Wu

The experimental results reveal the severity of ES Attack: i) ES Attack successfully steals the victim model without data hurdles, and ES Attack even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending ES Attack; iii) ES Attack facilitates further attacks relying on the stolen model.

BIG-bench Machine Learning

Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection

1 code implementation11 Sep 2020 Lan Zhang, Peng Liu, Yoon-Ho Choi, Ping Chen

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective.

Malware Detection reinforcement-learning +1

Secure Transmission by Leveraging Multiple Intelligent Reflecting Surfaces in MISO Systems

no code implementations9 Aug 2020 Jian Li, Lan Zhang, Kaiping Xue, Yuguang Fang

Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternative optimization method to decouple it into multiple sub-problems.

A Novel Decision Tree for Depression Recognition in Speech

no code implementations22 Feb 2020 Zhenyu Liu, Dongyu Wang, Lan Zhang, Bin Hu

Depression is a common mental disorder worldwide which causes a range of serious outcomes.

Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

no code implementations8 Feb 2020 Mu Yuan, Lan Zhang, Xiang-Yang Li, Hui Xiong

With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e. g., the number of high-confidence labels).

Image Retrieval Management +3

Using Deep Learning to Solve Computer Security Challenges: A Survey

no code implementations12 Dec 2019 Yoon-Ho Choi, Peng Liu, Zitong Shang, Haizhou Wang, Zhilong Wang, Lan Zhang, Junwei Zhou, Qingtian Zou

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community.

Cryptography and Security

A Resolution Prover for Coalition Logic

no code implementations3 Apr 2014 Cláudia Nalon, Lan Zhang, Clare Dixon, Ullrich Hustadt

We present a prototype tool for automated reasoning for Coalition Logic, a non-normal modal logic that can be used for reasoning about cooperative agency.

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