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Google发布的第二代深度学习系统TensorFlow
On the Cost of Model-Serving Frameworks: An Experimental Evaluation
Arxiv
0+阅读 · 11月15日
Characterising Open Source Co-opetition in Company-hosted Open Source Software Projects: The Cases of PyTorch, TensorFlow, and Transformers
Arxiv
0+阅读 · 10月23日
Recursive Function Definitions in Static Dataflow Graphs and their Implementation in TensorFlow
Arxiv
0+阅读 · 10月26日
Checker Bug Detection and Repair in Deep Learning Libraries
Arxiv
0+阅读 · 10月9日
Toward Understanding Deep Learning Framework Bugs
Arxiv
0+阅读 · 8月21日
Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
Arxiv
0+阅读 · 7月7日
KHNNs: hypercomplex neural networks computations via Keras using TensorFlow and PyTorch
Arxiv
0+阅读 · 6月29日
KerasCV and KerasNLP: Vision and Language Power-Ups
Arxiv
0+阅读 · 6月5日
KerasCV and KerasNLP: Vision and Language Power-Ups
Arxiv
0+阅读 · 5月31日
KerasCV and KerasNLP: Vision and Language Power-Ups
Arxiv
0+阅读 · 5月30日
A Partial Replication of MaskFormer in TensorFlow on TPUs for the TensorFlow Model Garden
Arxiv
0+阅读 · 4月29日
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Arxiv
0+阅读 · 2月10日
Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
Arxiv
0+阅读 · 2月1日
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Arxiv
0+阅读 · 1月30日
Security Knowledge-Guided Fuzzing of Deep Learning Libraries
Arxiv
0+阅读 · 2023年12月24日
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