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01/23/2022
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Imperative programming allows users to implement their deep neural netwo...
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12/04/2020
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
Deep learning (DL) frameworks take advantage of GPUs to improve the spee...
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09/28/2020
Accelerating Multi-Model Inference by Merging DNNs of Different Weights
Standardized DNN models that have been proved to perform well on machine...
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06/22/2020
Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees
Hyper-parameter optimization is crucial for pushing the accuracy of a de...
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11/24/2019
Stage-based Hyper-parameter Optimization for Deep Learning
As deep learning techniques advance more than ever, hyper-parameter opti...
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06/10/2019
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Classical Machine Learning (ML) pipelines often comprise of multiple ML ...
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12/04/2018
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
The rapid evolution of deep neural networks is demanding deep learning (...
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10/14/2018
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformatio...
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09/04/2018
Improving the Expressiveness of Deep Learning Frameworks with Recursion
Recursive neural networks have widely been used by researchers to handle...
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08/08/2018
Parallax: Automatic Data-Parallel Training of Deep Neural Networks
The employment of high-performance servers and GPU accelerators for trai...
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09/30/2010
Mantis: Predicting System Performance through Program Analysis and Modeling
We present Mantis, a new framework that automatically predicts program p...
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09/16/2010
CloneCloud: Boosting Mobile Device Applications Through Cloud Clone Execution
Mobile applications are becoming increasingly ubiquitous and provide eve...
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02/01/2010