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Found 5 papers, 1 papers with code
Accelerating Multi-Model Inference by Merging DNNs of Different Weights
Standardized DNN models that have been proved to perform well on machine learning tasks are widely used and often adopted as-is to solve downstream tasks, forming the transfer learning paradigm.
Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees
Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits.
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.
Improving the Expressiveness of Deep Learning Frameworks with Recursion
Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data.
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
