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Found 12 papers, 3 papers with code
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Recently, several systems have been proposed to combine the usability of imperative programming with the optimized performance of symbolic graph execution.
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search
We propose Supernet with Unbiased Meta-Features for Neural Architecture Search (SUMNAS), a supernet learning strategy based on meta-learning to tackle the knowledge forgetting issue.
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
Ideally, DL frameworks should be able to fully utilize the computation power of GPUs such that the running time depends on the amount of computation assigned to GPUs.
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.
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.
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation.
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.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
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
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
Our two SPORE algorithms are able to build relationships between responses (e. g., the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable.
