Lessons Learned from Developing Automated Machine Learning on HPC
This presentation by Romain EGELE explores various aspects of developing automated machine learning on High-Performance Computing (HPC) systems. Topics covered include multi-fidelity optimization, hyperparameters, model evaluation methods, learning curve extrapolation, and more valuable insights for
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Enhancing Distributional Similarity: Lessons from Word Embeddings
Explore how word vectors enable easy computation of similarity and relatedness, along with approaches for representing words using distributional semantics. Discover the contributions of word embeddings through novel algorithms and hyperparameters for improved performance.
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