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
1 views • 12 slides
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.
0 views • 69 slides
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
0 views • 12 slides
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
0 views • 15 slides
Introduction to Torch Deep Learning Package
Torch is a powerful deep learning package developed by Ronan Collobert. It supports various languages and is widely used in universities and research labs for large-scale learning in speech, image, and video applications. Torch enables setting up and training deep networks with configurable hyperpar
0 views • 36 slides
Optimizing Neural Network Hyperparameters for Jet Feature Analysis
Explore the challenges faced during the training of neural networks for jet feature analysis, including issues with learning capabilities and flat losses. Solutions and progress updates are discussed through various hyperparameter adjustments and problem resolutions, ultimately aiming to improve mod
0 views • 15 slides
Machine Learning Model Selection and Bias-Variance Tradeoff
Explore the concepts of model selection, error decomposition, and the bias-variance tradeoff in machine learning. Learn about techniques such as Naive Bayes classification and how to navigate issues like overfitting and underfitting. Dive into the importance of picking the right model class, hyperpa
0 views • 39 slides
Optimizing Network Performance with Hyperparameters and Temporal Features
Explore the intricate world of hyperparameter optimization, combining temporal and non-temporal features in network construction. Dive into learning rates, optimizers, network architecture, activation functions, and more for building your first efficient network. Get ready to train a network and sub
0 views • 5 slides