Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Overview

Pruner for nested cross-validation - Sphinx-Doc

MIT License pytest Static Code Checks GitHub issues

Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes. Standard pruning algorithms to accelerate hyperparameter optimization must prune late or risk aborting computations of promising hyperparameter sets due to high variance in the performance evaluation metric. The cv-pruner allows combining a comparison-based pruning strategy with two additional pruning strategies based on domain or prior knowledge. One of them prunes semantically meaningless trials. The other is a threshold-based pruning strategy that extrapolates the performance evaluation metric. The combination of pruning strategies can lead to a massive speedup in computation.

Installation

CV-Pruner is available at the Python Package Index (PyPI). It can be installed with pip:

$ pip install cv-pruner

Licensing

Copyright (c) 2021 Sigrun May, Helmholtz-Zentrum für Infektionsforschung GmbH (HZI)
Copyright (c) 2021 Sigrun May, Ostfalia Hochschule für angewandte Wissenschaften

Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License by reviewing the file LICENSE in the repository.

You might also like...
The implementation for paper Joint t-SNE for Comparable Projections of Multiple High-Dimensional Datasets.

Joint t-sne This is the implementation for paper Joint t-SNE for Comparable Projections of Multiple High-Dimensional Datasets. abstract: We present Jo

Genetic feature selection module for scikit-learn

sklearn-genetic Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal valu

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.
In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

Defending against Model Stealing via Verifying Embedded External Features
Defending against Model Stealing via Verifying Embedded External Features

Defending against Model Stealing Attacks via Verifying Embedded External Features This is the official implementation of our paper Defending against M

The code is for the paper
The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

Exploring whether attention is necessary for vision transformers
Exploring whether attention is necessary for vision transformers

Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet Paper/Report TL;DR We replace the attention layer in a v

Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

This repository contains the code and models necessary to replicate the results of paper:  How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

Releases(0.0.1rc3)
A benchmark dataset for mesh multi-label-classification based on cube engravings introduced in MeshCNN

Double Cube Engravings This script creates a dataset for multi-label mesh clasification, with an intentionally difficult setup for point cloud classif

Yotam Erel 1 Nov 30, 2021
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".

The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergen

281 Dec 30, 2022
Unofficial Pytorch Implementation of WaveGrad2

WaveGrad 2 — Unofficial PyTorch Implementation WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis Unofficial PyTorch+Lightning Implementati

MINDs Lab 104 Nov 29, 2022
Marvis is Mastouri's Jarvis version of the AI-powered Python personal assistant.

Marvis v1.0 Marvis is Mastouri's Jarvis version of the AI-powered Python personal assistant. About M.A.R.V.I.S. J.A.R.V.I.S. is a fictional character

Reda Mastouri 1 Dec 29, 2021
CVPR 2021: "The Spatially-Correlative Loss for Various Image Translation Tasks"

Spatially-Correlative Loss arXiv | website We provide the Pytorch implementation of "The Spatially-Correlative Loss for Various Image Translation Task

Chuanxia Zheng 89 Jan 04, 2023
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

Jamie Simon 45 Dec 02, 2022
MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

mediapipe-python-sample MediaPipeのPythonパッケージのサンプルです。 2020/12/11時点でPython実装のある以下4機能について用意しています。 Hands Pose Face Mesh Holistic Requirement mediapipe 0.

KazuhitoTakahashi 217 Dec 12, 2022
Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation [Arxiv] [Paper] As acquiring pixel-wise an

Lukas Hoyer 305 Dec 29, 2022
[NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators"

G-PATE This is the official code base for our NeurIPS 2021 paper: "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of T

AI Secure 14 Oct 12, 2022
A MNIST-like fashion product database. Benchmark

Fashion-MNIST Table of Contents Why we made Fashion-MNIST Get the Data Usage Benchmark Visualization Contributing Contact Citing Fashion-MNIST License

Zalando Research 10.5k Jan 08, 2023
Pyramid Scene Parsing Network, CVPR2017.

Pyramid Scene Parsing Network by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. Introduction This

Hengshuang Zhao 1.5k Jan 05, 2023
Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks

1xN Pattern for Pruning Convolutional Neural Networks (paper) . This is Pytorch re-implementation of "1xN Pattern for Pruning Convolutional Neural Net

Mingbao Lin (林明宝) 29 Nov 29, 2022
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
Fast Soft Color Segmentation

Fast Soft Color Segmentation

3 Oct 29, 2022
An implementation of "Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport"

Optex An implementation of Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport for TU Delft CS4240. You c

Hans Brouwer 33 Jan 05, 2023
HandTailor: Towards High-Precision Monocular 3D Hand Recovery

HandTailor This repository is the implementation code and model of the paper "HandTailor: Towards High-Precision Monocular 3D Hand Recovery" (arXiv) G

Lv Jun 113 Jan 06, 2023
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks This folder contains the code to reproduce the data in "The Implicit Bias o

Samuel Lippl 0 Feb 05, 2022
Code for ICLR 2020 paper "VL-BERT: Pre-training of Generic Visual-Linguistic Representations".

VL-BERT By Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai. This repository is an official implementation of the paper VL-BERT:

Weijie Su 698 Dec 18, 2022
A task Provided by A respective Artenal Ai and Ml based Company to complete it

A task Provided by A respective Alternal Ai and Ml based Company to complete it .

Parth Madan 1 Jan 25, 2022