Search Results for author:
Found 6 papers, 1 papers with code
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features.
TypeFly: Flying Drones with Large Language Model
In this paper, we present a system called TypeFly that tackles the above three problems using a combination of edge-based vision intelligence, novel programming language design, and prompt engineering.
Prompt Cache: Modular Attention Reuse for Low-Latency Inference
We present Prompt Cache, an approach for accelerating inference for large language models (LLM) by reusing attention states across different LLM prompts.
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy
This work departs from prior works in methodology: we leverage adversarial learning to a better balance between privacy and utility.
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
This work represents a methodical departure from prior works: we balance between a measure of privacy and another of utility by leveraging adversarial learning to find a sweeter tradeoff.
Handling Noise in Single Image Deblurring Using Directional Filters
Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image.
