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Found 13 papers, 3 papers with code
Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment
Personalized fashion recommendation is a difficult task because 1) the decisions are highly correlated with users' aesthetic appetite, which previous work frequently overlooks, and 2) many new items are constantly rolling out that cause strict cold-start problems in the popular identity (ID)-based recommendation methods.
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise.
Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models
We introduce the MTRB (massive tool retrieval benchmark) to evaluate real-world tool-augmented LLM scenarios with a large number of tools.
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare.
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data.
Machine Learning with Confidential Computing: A Systematization of Knowledge
Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces.
Towards Battery-Free Machine Learning and Inference in Underwater Environments
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments?
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Our proposed framework enables clients to localize and quantify the private information leakage in a layer-wise manner, and enables a better understanding of the sources of information leakage in collaborative learning, which can be used by future studies to benchmark new attacks and defense mechanisms.
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs).
Towards Characterizing and Limiting Information Exposure in DNN Layers
Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models.
Weight-importance sparse training in keyword spotting
Besides this, at most of time, ASR system is used to deal with real-time problem such as keyword spotting (KWS).
