28 citations · 47 across the 9 of their papers we have counts for
10 papers
VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement
Tiancheng Fang, Bowen Pan, Lingxi Chen +4
We propose VIAFormer, a Voxel-Image Alignment Transformer model designed for Multi-view Conditioned Voxel Refinement--the task of repairing incomplete noisy voxels using calibrated…
CORE: Reducing UI Exposure in Mobile Agents via Collaboration Between Cloud and Local LLMs
Gucongcong Fan, Chaoyue Niu, Chengfei Lyu +2
Mobile agents rely on Large Language Models (LLMs) to plan and execute tasks on smartphone user interfaces (UIs). While cloud-based LLMs achieve high task accuracy, they require up…
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Xiang Chen, Duanzheng Song, Honghao Gui +7
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in…
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models
Yucheng Ding, Chaoyue Niu, Fan Wu +3
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage th…
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design
Yikai Yan, Chaoyue Niu, Fan Wu +4
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with h…
On-Device Model Fine-Tuning with Label Correction in Recommender Systems
Yucheng Ding, Chaoyue Niu, Fan Wu +3
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mob…
On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems
Renjie Gu, Chaoyue Niu, Yikai Yan +5
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to eac…
Federated Submodel Optimization for Hot and Cold Data Features
Yucheng Ding, Chaoyue Niu, Fan Wu +4
We study practical data characteristics underlying federated learning, where non-i.i.d. data from clients have sparse features, and a certain client's local data normally involves…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…
From Server-Based to Client-Based Machine Learning: A Comprehensive Survey
Renjie Gu, Chaoyue Niu, Fan Wu +4
In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning…