deep-ensemble
There are 15 repositories under deep-ensemble topic.
FreeformRobotics/Divide-and-Co-training
[TIP 2022] Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training. Plus, an image classification toolbox includes ResNet, Wide-ResNet, ResNeXt, ResNeSt, ResNeXSt, SENet, Shake-Shake, DenseNet, PyramidNet, and EfficientNet.
SamsungLabs/gps-augment
Simple but high-performing method for learning a policy of test-time augmentation
Frightera/Sample-Machine-Learning-Projects
Some example projects that was made using Tensorflow (mostly). This repository contains the projects that I've experimented-tried when I was new in Deep Learning.
akashmondal1810/UncertaintyEstimation
Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods
bond005/yandex-shifts-weather
The best solution of the Weather Prediction track in the Yandex Shifts challenge
AI4PFAS/AI4PFAS
Dataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"
antonbaumann/MIMO-Unet
PyTorch implementation of Probabilistic MIMO U-Net
releaunifreiburg/DeepRankingEnsembles
[ICLR 2023] Deep Ranking Ensembles for Hyperparameter Optimization
wiguider/Ensemble-Deep-Learning-to-Classify-Scoliosis-and-Healthy-Subjects
An Ensemble Deep Learning Model to Classify Scoliosis and Healthy Subjects Based on Non-invasive Rasterstereography Analysis
Dounia18/EGB
Full-Reference Image Quality Assessment models based on ensemble of gradient boosting
Chandureddy8/Ensemble-MRI-Parkinson-s-Detection-
early detection method for parkinson's disease using deep ensemble learning on MRI dataset
acen20/deep-ensemble-jet
Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose
lab-conrad/resVAE-ensemble
resVAE-ensemble: feature identification in single-cell data
yunshengtian/BE-CBO
[ICML 2024] Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
anita76/AM207-Project
A pedagogical study and analysis of the results from "Deep Ensembles: A Loss Landscape Perspective".