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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Two-stage Federated Phenotyping and Patient Representation Learning ACL 2019
Transferable AutoML by Model Sharing Over Grouped Datasets CVPR 2019
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients NIPS 2019
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism NIPS 2019
SySCD: A System-Aware Parallel Coordinate Descent Algorithm NIPS 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization NIPS 2019
SepNE: Bringing Separability to Network Embedding AAAI 2019
Robust and Communication-Efficient Collaborative Learning NIPS 2019
On Distributed Averaging for Stochastic k-PCA NIPS 2019
Communication-efficient Distributed SGD with Sketching NIPS 2019
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning NIPS 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NIPS 2019
FreeFlow: Software-based Virtual RDMA Networking for Containerized Clouds NSDI 2019
Tiresias: A GPU Cluster Manager for Distributed Deep Learning NSDI 2019
Improved Parallel Algorithms for Density-Based Network Clustering ICML 2019
Exploiting Commutativity For Practical Fast Replication NSDI 2019
Achieving the time of 1-NN, but the accuracy of k-NN AISTATS 2018
Gradient Coding from Cyclic MDS Codes and Expander Graphs ICML 2018
A Distributed Second-Order Algorithm You Can Trust ICML 2018
A Practical Algorithm for Distributed Clustering and Outlier Detection NIPS 2018
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018
cpSGD: Communication-efficient and differentially-private distributed SGD NIPS 2018
Proximal SCOPE for Distributed Sparse Learning NIPS 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NIPS 2018
Training Neural Networks Using Features Replay NIPS 2018