6 citations · 11 across the 17 of their papers we have counts for
17 papers
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3
Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermediate predictions that determ…
An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse
Yuan Cao, Dezhi Ran, Yuzhe Guo +5
Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we o…
PARD: Enhancing Goodput for Inference Pipeline via Proactive Request Dropping
Zhixin Zhao, Yitao Hu, Simin Chen +8
Modern deep neural network (DNN) applications integrate multiple DNN models into inference pipelines with stringent latency requirements for customized tasks. To mitigate extensive…
Efficiency Robustness of Dynamic Deep Learning Systems
Ravishka Rathnasuriya, Tingxi Li, Zexin Xu +4
Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address eff…
Impact Analysis of Inference Time Attack of Perception Sensors on Autonomous Vehicles
Hanlin Chen, Simin Chen, Wenyu Li +2
As a safety-critical cyber-physical system, cybersecurity and related safety issues for Autonomous Vehicles (AVs) have been important research topics for a while. Among all the mod…
PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
Simin Chen, Xiaoning Feng, Xiaohong Han +2
In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Ben…
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study
Yufei Li, Simin Chen, Yanghong Guo +3
Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code dis…
DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization
Simin Chen, Shiyi Wei, Cong Liu +1
DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then…
SlothSpeech: Denial-of-service Attack Against Speech Recognition Models
Mirazul Haque, Rutvij Shah, Simin Chen +3
Deep Learning (DL) models have been popular nowadays to execute different speech-related tasks, including automatic speech recognition (ASR). As ASR is being used in different real…
Dynamic Transformers Provide a False Sense of Efficiency
Yiming Chen, Simin Chen, Zexin Li +4
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference. Multi-exit is a mainstream appr…
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
Zexin Li, Bangjie Yin, Taiping Yao +4
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information…
NHtapDB: Native HTAP Databases
Guoxin Kang, Lei Wang, Simin Chen +1
Native database (1) provides a near-data machine learning framework to facilitate generating real-time business insight, and predefined change thresholds will trigger online traini…
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
Simin Chen, Mirazul Haque, Cong Liu +1
Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used on resource-constrained embedded devices. We observe that, similar to traditional software, redu…
LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models
Xiaoning Feng, Xiaohong Han, Simin Chen +1
In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art LLMs. By analyzing the working mechanism and impleme…
Learning to Reverse DNNs from AI Programs Automatically
Simin Chen, Hamed Khanpour, Cong Liu +1
With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern. To quantify the model leakage risk of on-device DNNs autom…
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
Simin Chen, Zihe Song, Mirazul Haque +2
Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work…
Estimating Predictive Uncertainty Under Program Data Distribution Shift
Yufei Li, Simin Chen, Wei Yang
Deep learning (DL) techniques have achieved great success in predictive accuracy in a variety of tasks, but deep neural networks (DNNs) are shown to produce highly overconfident sc…