Search Results for author: Chunyang Chen

Found 18 papers, 9 papers with code

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

no code implementations26 Feb 2024 Han Wang, Sijia Yu, Chunyang Chen, Burak Turhan, Xiaodong Zhu

Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness.

On the Semantics of LM Latent Space: A Vocabulary-defined Approach

no code implementations29 Jan 2024 Jian Gu, Aldeida Aleti, Chunyang Chen, Hongyu Zhang

In response, we introduce a pioneering method called vocabulary-defined semantics, which establishes a reference frame within the LM latent space, ensuring disentangled semantic analysis grounded in LM vocabulary.


Neuron-level LLM Patching for Code Generation

no code implementations8 Dec 2023 Jian Gu, Aldeida Aleti, Chunyang Chen, Hongyu Zhang

In this paper, we propose a novel and effective model editing approach, \textsc{MENT}, to patch LLMs in coding tasks.

Code Generation Model Editing

Enhancing Virtual Assistant Intelligence: Precise Area Targeting for Instance-level User Intents beyond Metadata

no code implementations7 Jun 2023 Mengyu Chen, Zhenchang Xing, Jieshan Chen, Chunyang Chen, Qinghua Lu

Although their capabilities of processing user intents have been developed rapidly, virtual assistants in most platforms are only capable of handling pre-defined high-level tasks supported by extra manual efforts of developers.

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

1 code implementation NeurIPS 2023 Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan

Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.

Graph Learning

Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

no code implementations23 Feb 2023 Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan

Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.

Graph Learning Neural Architecture Search

Training-free Lexical Backdoor Attacks on Language Models

1 code implementation8 Feb 2023 Yujin Huang, Terry Yue Zhuo, Qiongkai Xu, Han Hu, Xingliang Yuan, Chunyang Chen

In this work, we propose Training-Free Lexical Backdoor Attack (TFLexAttack) as the first training-free backdoor attack on language models.

Backdoor Attack Data Poisoning +1

Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity

no code implementations30 Jan 2023 Terry Yue Zhuo, Yujin Huang, Chunyang Chen, Zhenchang Xing

We believe that our findings may give light on future efforts to determine and mitigate the ethical hazards posed by machines in LLM applications.

Ethics Language Modelling

Psychologically-Inspired, Unsupervised Inference of Perceptual Groups of GUI Widgets from GUI Images

1 code implementation15 Jun 2022 Mulong Xie, Zhenchang Xing, Sidong Feng, Chunyang Chen, Liming Zhu, Xiwei Xu

These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasant and usability.

Smart App Attack: Hacking Deep Learning Models in Android Apps

1 code implementation23 Apr 2022 Yujin Huang, Chunyang Chen

We evaluate the attack effectiveness and generality in terms of four different settings including pre-trained models, datasets, transfer learning approaches and adversarial attack algorithms.

Adversarial Attack Binary Classification +1

Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow

1 code implementation1 Feb 2021 Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen

As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning.

GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks

1 code implementation25 Jan 2021 Tianming Zhao, Chunyang Chen, Yuanning Liu, Xiaodong Zhu

Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications, and online websites.

Image Generation Text Generation

DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection

no code implementations18 Jan 2021 Yuanchun Li, Jiayi Hua, Haoyu Wang, Chunyang Chen, Yunxin Liu

The core of the attack is a neural conditional branch constructed with a trigger detector and several operators and injected into the victim model as a malicious payload.

Backdoor Attack

Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps

1 code implementation12 Jan 2021 Yujin Huang, Han Hu, Chunyang Chen

Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition.

Adversarial Attack Image Classification +3

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

2 code implementations12 Aug 2020 Jieshan Chen, Mulong Xie, Zhenchang Xing, Chunyang Chen, Xiwei Xu, Liming Zhu, Guoqiang Li

We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods.

Code Generation object-detection +1

Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning

1 code implementation1 Mar 2020 Jieshan Chen, Chunyang Chen, Zhenchang Xing, Xiwei Xu, Liming Zhu, Guoqiang Li, Jinshui Wang

However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app.

Missing Labels

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

no code implementations20 Mar 2018 Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data.

Adversarial Attack Defect Detection

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