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Found 4 papers, 1 papers with code
Deep Collective Knowledge Distillation
We propose deep collective knowledge distillation for model compression, called DCKD, which is a method for training student models with rich information to acquire knowledge from not only their teacher model but also other student models.
Stage-based Hyper-parameter Optimization for Deep Learning
As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters.
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to support straightforward programming models for quickly implementing and experimenting with complex network structures.
Parallax: Automatic Data-Parallel Training of Deep Neural Networks
The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in machine learning (ML).
Distributed, Parallel, and Cluster Computing
