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A Data-Centric Optimization Framework for Machine Learning
DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks. -
Adam: A method for stochastic optimization
This dataset is used to test the robustness of watermarking methods against adaptive attacks. -
BENCHMARK ASSESSMENT FOR DEEPSPEED OPTIMIZATION LIBRARY
Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance...
