Related references
Note: Only part of the references are listed.FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization
Weijie Fu et al.
IEEE TRANSACTIONS ON BIG DATA (2022)
A Survey of Optimization Methods From a Machine Learning Perspective
Shiliang Sun et al.
IEEE TRANSACTIONS ON CYBERNETICS (2020)
Fast Matrix Factorization With Nonuniform Weights on Missing Data
Xiangnan He et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)
Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks
Mahdi Soltanolkotabi et al.
IEEE TRANSACTIONS ON INFORMATION THEORY (2019)
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin
NATURE MACHINE INTELLIGENCE (2019)
PS2: Parameter Server on Spark
Zhipeng Zhang et al.
SIGMOD '19: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (2019)
Angel: a new large-scale machine learning system
Jie Jiang et al.
NATIONAL SCIENCE REVIEW (2018)
A brief introduction to weakly supervised learning
Zhi-Hua Zhou
NATIONAL SCIENCE REVIEW (2018)
High-Level Programming Abstractions for Distributed Graph Processing
Vasiliki Kalavri et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2018)
A Survey on Learning to Hash
Jingdong Wang et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)
A survey on deep learning for big data
Qingchen Zhang et al.
INFORMATION FUSION (2018)
Optimization Methods for Large-Scale Machine Learning
Leon Bottou et al.
SIAM REVIEW (2018)
DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions
Jiawei Jiang et al.
SIGMOD'18: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (2018)
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs
Rong Chen et al.
ACM TRANSACTIONS ON PARALLEL COMPUTING (2018)
Scalable Active Learning by Approximated Error Reduction
Weijie Fu et al.
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)
Learning on Big Graph: Label Inference and Regularization with Anchor Hierarchy
Meng Wang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
Jinkyu Kim et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)
RANDOMIZED SKETCHES FOR KERNELS: FAST AND OPTIMAL NONPARAMETRIC REGRESSION
Yun Yang et al.
ANNALS OF STATISTICS (2017)
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Rolf Jagerman et al.
SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (2017)
Deformable Convolutional Networks
Jifeng Dai et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz et al.
CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (2017)
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
Immanuel Bayer et al.
PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17) (2017)
Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization
Meng Wang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2016)
Towards Comprehensive Traffic Forecasting in Cloud Computing: Design and Application
Yang Peng et al.
IEEE-ACM TRANSACTIONS ON NETWORKING (2016)
Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems
Shan Sung Liew et al.
NEUROCOMPUTING (2016)
A STOCHASTIC QUASI-NEWTON METHOD FOR LARGE-SCALE OPTIMIZATION
R. H. Byrd et al.
SIAM JOURNAL ON OPTIMIZATION (2016)
SystemML: Declarative Machine Learning on Spark
Matthias Boehm et al.
PROCEEDINGS OF THE VLDB ENDOWMENT (2016)
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)
Coordinate descent algorithms
Stephen J. Wright
MATHEMATICAL PROGRAMMING (2015)
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Kai Zhang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2015)
A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems
Wei-Sheng Chin et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2015)
SINGA: A Distributed Deep Learning Platform
Beng Chin Ooi et al.
MM'15: PROCEEDINGS OF THE 2015 ACM MULTIMEDIA CONFERENCE (2015)
Efficient Machine Learning for Big Data: A Review
Omar Y. Al-Jarrah et al.
BIG DATA RESEARCH (2015)
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Sungjin Ahn et al.
KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2015)
Data Mining with Big Data
Xindong Wu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)
Good Practice in Large-Scale Learning for Image Classification
Zeynep Akata et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2014)
Coarse-to-fine Categorization of Visual Scenes in Scene-selective Cortex
Benoit Musel et al.
JOURNAL OF COGNITIVE NEUROSCIENCE (2014)
Big Data: A Survey
Min Chen et al.
MOBILE NETWORKS & APPLICATIONS (2014)
Quantum Support Vector Machine for Big Data Classification
Patrick Rebentrost et al.
PHYSICAL REVIEW LETTERS (2014)
Big Data Deep Learning: Challenges and Perspectives
Xue-Wen Chen et al.
IEEE ACCESS (2014)
Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML
Matthias Boehm et al.
PROCEEDINGS OF THE VLDB ENDOWMENT (2014)
LSD-SLAM: Large-Scale Direct Monocular SLAM
Jakob Engel et al.
COMPUTER VISION - ECCV 2014, PT II (2014)
A tutorial survey of architectures, algorithms, and applications for deep learning
Li Deng
APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING (2014)
A Comparison of Platforms for Implementing and Running Very Large Scale Machine Learning Algorithms
Zhuhua Cai et al.
SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (2014)
Hierarchical Subquery Evaluation for Active Learning on a Graph
Oisin Mac Aodha et al.
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2014)
Representation Learning: A Review and New Perspectives
Yoshua Bengio et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)
Gradient methods for minimizing composite functions
Yu Nesterov
MATHEMATICAL PROGRAMMING (2013)
Scalable Active Learning for Multiclass Image Classification
Ajay J. Joshi et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2012)
Recent Advances of Large-Scale Linear Classification
Guo-Xun Yuan et al.
PROCEEDINGS OF THE IEEE (2012)
EFFICIENCY OF COORDINATE DESCENT METHODS ON HUGE-SCALE OPTIMIZATION PROBLEMS
Yu Nesterov
SIAM JOURNAL ON OPTIMIZATION (2012)
Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
Yucheng Low et al.
PROCEEDINGS OF THE VLDB ENDOWMENT (2012)
A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson et al.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS (2011)
A coordinate gradient descent method for a 1-regularized convex minimization
Sangwoon Yun et al.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS (2011)
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
N. Halko et al.
SIAM REVIEW (2011)
LabelMe: Online Image Annotation and Applications
Antonio Torralba et al.
PROCEEDINGS OF THE IEEE (2010)
Bandwidth optimal all-reduce algorithms for clusters of workstations
Pitch Patarasuk et al.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING (2009)
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Amir Beck et al.
SIAM JOURNAL ON IMAGING SCIENCES (2009)
Mapreduce: Simplified data processing on large clusters
Jeffrey Dean et al.
COMMUNICATIONS OF THE ACM (2008)
Finding scientific topics
TL Griffiths et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2004)
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
I Daubechies et al.
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS (2004)
Stochastic gradient boosting
JH Friedman
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)
Additive logistic regression: A statistical view of boosting
J Friedman et al.
ANNALS OF STATISTICS (2000)
Share Paper
