ML-Ensemble – high performance ensemble learning
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Updated
Nov 13, 2023 - Python
ML-Ensemble – high performance ensemble learning
tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
Open-source framework for uncertainty and deep learning models in PyTorch 🌱
Pytorch implementation of our NeurIPS'20 *Oral* paper "DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles".
Random Forests in Apache Spark
Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/
SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
Solution for ENS - Societe Generale Challenge (1st place).
Concepts used: kNN, SVM, boosting (XGBoost, Gradient boosting, Light GBM, AdaBoost, Random Forests), deep learning (CNN, LSTM), ensembles (model stacking), transfer learning.
Model stacking for predictive ensembles
Large-scale atmospheric response to Antarctic sea ice loss
Random forests ported to Javascript with WebAssembly and WebWorkers
PAMIP simulations to understand role of sea surface temperatures on polar amplifications
Simple but high-performing method for learning a policy of test-time augmentation
Decision and Ensemble methods implemented in C#
Tensorflow slim based model training for ImageCLEF 2016 subfigure classification.
Analysis of Insurance Liability Claim Amount for settlement
Methods for increasing generalization ability based on different ways of ensembles building
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