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DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
The rapidly growing size of deep neural network (DNN) models and datasets has given rise to a variety of distribution strategies such as data, tensor-model, pipeline parallelism, and hybrid combinations thereof.
Learning Invariants using Decision Trees
This is a standard problem in machine learning: given a sample of good and bad points, one is asked to find a classifier that generalizes from the sample and separates the two sets.
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