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Found 17 papers, 8 papers with code
Exploring the Impact of In-Browser Deep Learning Inference on Quality of User Experience and Performance
Additionally, we noticed that in-browser inference increases the time it takes for graphical user interface (GUI) components to load in web browsers by a significant 67. 2\%, which severely impacts the overall QoE for users of web applications that depend on this technology.
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients through Just-in-Time Kernel Optimizations
Web applications are increasingly becoming the primary platform for AI service delivery, making in-browser deep learning (DL) inference more prominent.
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environments.
SwapMoE: Efficient Memory-Constrained Serving of Large Sparse MoE Models via Dynamic Expert Pruning and Swapping
The main idea of SwapMoE is to keep a small dynamic set of important experts, namely Virtual Experts, in the main memory for inference, while seamlessly maintaining how the Virtual Experts map to the actual experts.
AutoDroid: LLM-powered Task Automation in Android
Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones.
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
However, most backdoor attacks have to modify the neural network models through training with poisoned data and/or direct model editing, which leads to a common but false belief that backdoor attack can be easily avoided by properly protecting the model.
DroidBot-GPT: GPT-powered UI Automation for Android
Given a natural language description of a desired task, DroidBot-GPT can automatically generate and execute actions that navigate the app to complete the task.
AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments
Model elastification generates a high-quality search space of model architectures with the guidance of a developer-specified oracle model.
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.
PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing
Vision Transformer (ViT) is known to be highly nonlinear like other classical neural networks and could be easily fooled by both natural and adversarial patch perturbations.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.
Representational Continuity for Unsupervised Continual Learning
Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge.
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation.
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
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.
Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware
To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community.
Cryptography and Security
