64 citations · 252 across the 64 of their papers we have counts for
9 papers · 1 filter
SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition
Hao Li, Zixuan Li, Kenli Li +3
Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the \underline{H}igh-\under…
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi +2
Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number…
An Exploratory Analysis on Users' Contributions in Federated Learning
Jiyue Huang, Rania Talbi, Zilong Zhao +3
Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core pr…
A Unified Seeding Framework
Ya-Wen Teng, Hsi-Wen Chen, De-Nian Yang +3
Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds…
Active Learning for Noisy Data Streams Using Weak and Strong Labelers
Taraneh Younesian, Dick Epema, Lydia Y. Chen
Labeling data correctly is an expensive and challenging task in machine learning, especially for on-line data streams. Deep learning models especially require a large number of cle…
PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters
Isabelly Rocha, Nathaniel Morris, Lydia Y. Chen +3
DNN learning jobs are common in today's clusters due to the advances in AI driven services such as machine translation and image recognition. The most critical phase of these jobs…