30 citations · 90 across the 9 of their papers we have counts for
14 papers
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Chengfei Lv, Chaoyue Niu, Renjie Gu +17
To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system,…
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…
Normal Learning in Videos with Attention Prototype Network
Chao Hu, Fan Wu, Weijie Wu +2
Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection. With models trained on the normal data, the reconstructio…
Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descent
Fan Wu, Patrick Rebeschini
This paper studies early-stopped mirror descent applied to noisy sparse phase retrieval, which is the problem of recovering a -sparse signal fr…
Implicit Regularization in Matrix Sensing via Mirror Descent
Fan Wu, Patrick Rebeschini
We study discrete-time mirror descent applied to the unregularized empirical risk in matrix sensing. In both the general case of rectangular matrices and the particular case of pos…
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Fan Wu, Patrick Rebeschini
We analyze continuous-time mirror descent applied to sparse phase retrieval, which is the problem of recovering sparse signals from a set of magnitude-only measurements. We apply m…