Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
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Sep 26, 2022 - Python
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Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
An autoML framework & toolkit for machine learning on graphs.
machine-learning deep-learning pytorch automl neural-architecture-search hyper-parameter-optimization graph-neural-networks pytorch-geometric
Towards Generalized and Efficient Blackbox Optimization System/Package (KDD 2021 & JMLR 2024)
distributed-systems saas constrained-optimization multi-objective-optimization bayesian-optimization hyper-parameter-optimization blackbox-optimization automatic-machine-learning
DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images
machine-learning bioinformatics deep-learning cheminformatics drug-discovery drug-repurposing convolutional-neural-networks command-line-tool prediction-model performance-evaluation hyper-parameter-optimization 2d-images-of-compounds drug-target-interactions
A paper collection about automated graph learning
machine-learning deep-learning graph automl neural-architecture-search hyper-parameter-optimization graph-neural-networks
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
python distributed-systems data-science machine-learning deep-learning ensemble-learning bayesian-optimization neural-architecture-search meta-learning hyper-parameter-optimization blackbox-optimization automl-algorithms automl-pipeline knobs-tuning
Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades
cross-validation eda classification modelling beginner feature-engineering data-cleaning evaluation-metrics hyper-parameter-optimization machinelearning-python
Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.
python data-science scikit-learn hyper-parameter-optimization hyper-parameter-tuning nature-inspired-algorithms
Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
decision data-science hyperparameter-tuning decision-tree-classifier hyper-parameter-optimization post-pruning cost-complexity-pruning pruning-optimization
A gradient free optimization routine which combines Particle Swarm Optimization with a local optimization for each particle
Grammaropt : a framework for optimizing over domain specific languages (DSLs)
data-science machine-learning scikit-learn grammar-specification context-free-grammar automated-machine-learning hyper-parameter-optimization
thesis pattern-recognition dynamic-programming human-activity-recognition dynamic-time-warping distance-functions normalization movement-detection linear-interpolation multi-class-classification hyper-parameter-optimization subsequence-dynamic-time-warping sakoe-chiba-band keogh-lower-bounding
Pipelineopt, sckit-learn automatic pipeline optimization
data-science machine-learning scikit-learn context-free-grammar automated-machine-learning hyper-parameter-optimization
Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)
Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades
evaluation cross-validation eda classification modelling feature-engineering data-cleaning hyper-parameter-optimization begginer machinelearning-python
Pipoh is a library that implements several diversification techniques base on mean-variance framework. In addition, it includes a novel purely data-driven methods for determining the optimal value of the hyper-parameters associated with each investment strategy.
Python implementation that explores how different parameters impact a single hidden layer of a feed-forward neural network using gradient descent
python python3 hidden-layers hyper-parameter-optimization hyper-parameter feed-forward-neural-networks ffnet
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