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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Fast Bayesian Optimization for Machine Learning Hyperparameters on Large Datasets
Fast Bayesian Optimization optimizes hyperparameters for machine learning on large datasets efficiently. It involves black-box optimization using Gaussian Processes and acquisition functions. Regular Bayesian Optimization faces challenges with large datasets, but FABOLAS introduces an innovative app
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Dynamic Crowd Simulation Using Deep Reinforcement Learning and Bayesian Inference
This paper introduces a novel method for simulating crowd movements by combining deep reinforcement learning (DRL) with Bayesian inference. By leveraging neural networks to capture complex crowd behaviors, the proposed approach incorporates rewards for natural movements and a position-based dynamics
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