The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

Related tags

Deep Learningdata-sciencemachine-learningscikit-learnactive-learning
Overview

CircleCI Github Actions Codecov Documentation Status Pypi Version Black Python Versions DOI

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory.

Additionally, contributions at the algorithm level are available in the package mlresearch.

Installation

A Python distribution of version 3.8 or 3.9 is required to run this project. Due to the computational limitations of the free tiers in CI/CD platforms, currently we cannot ensure compatibility with earlier Python versions.

ML-Research requires:

  • numpy (>= 1.14.6)
  • pandas (>= 1.3.5)
  • sklearn (>= 1.0.0)
  • imblearn (>= 0.8.0)
  • rich (>= 10.16.1)
  • matplotlib (>= 2.2.3)
  • seaborn (>= 0.9.0)
  • rlearn (>= 0.2.1)
  • pytorch (>= 1.10.1)
  • torchvision (>= 0.11.2)
  • pytorch_lightning (>= 1.5.8)

User Installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip :

pip install -U ml-research

The documentation includes more detailed installation instructions.

Installing from source

The following commands should allow you to setup the development version of the project with minimal effort:

# Clone the project.
git clone https://github.com/joaopfonseca/ml-research.git
cd ml-research

# Create and activate an environment 
make environment 
conda activate mlresearch # Adapt this line accordingly if you're not running conda

# Install project requirements and the research package
pip install .[tests,docs]

Citing ML-Research

If you use ML-Research in a scientific publication, we would appreciate citations to the following paper:

@article{Fonseca2021,
  doi = {10.3390/RS13132619},
  url = {https://doi.org/10.3390/RS13132619},
  keywords = {SMOTE,active learning,artificial data generation,land use/land cover classification,oversampling},
  year = {2021},
  month = {jul},
  publisher = {Multidisciplinary Digital Publishing Institute},
  volume = {13},
  pages = {2619},
  author = {Fonseca, Joao and Douzas, Georgios and Bacao, Fernando},
  title = {{Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification}},
  journal = {Remote Sensing}
}
You might also like...
A collection of 100 Deep Learning images and visualizations
A collection of 100 Deep Learning images and visualizations

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

AI Summer 65 Sep 12, 2022
ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.
ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

ManimML ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

null 259 Jan 4, 2023
Easily pull telemetry data and create beautiful visualizations for analysis.
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.

Katsuya Hyodo 24 Mar 2, 2022
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

pyrelational is a python active learning library developed by Relation Therapeutics for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active learning strategies.

Relation Therapeutics 95 Dec 27, 2022
Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.
Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.

Rayvens augments Ray with events. With Rayvens, Ray applications can subscribe to event streams, process and produce events. Rayvens leverages Apache

CodeFlare 32 Dec 25, 2022
Memoized coduals - Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers
Memoized coduals - Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers

The dual numbers can do efficient autodiff! The codual numbers are a simple meth

wlad 2 Dec 19, 2022
python-timbl, originally developed by Sander Canisius, is a Python extension module wrapping the full TiMBL C++ programming interface. With this module, all functionality exposed through the C++ interface is also available to Python scripts. Being able to access the API from Python greatly facilitates prototyping TiMBL-based applications.

README: python-timbl Authors: Sander Canisius, Maarten van Gompel Contact: [email protected] Web site: https://github.com/proycon/python-timbl/ pytho

Maarten van Gompel 16 Jan 16, 2022
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

Sohil Shah 197 Nov 29, 2022
Comments
  • Consider modifying default BYOL hyper-parameters for smaller batch sizes

    Consider modifying default BYOL hyper-parameters for smaller batch sizes

    Applicable to both BYOL and SimSiam: Some hyperparameters might need to be added. Some are hard-coded to the default values.

    Taken from the BYOL paper: Screenshot from 2022-03-18 17-54-43

    opened by joaopfonseca 1
  • Remove computer vision models, augmentations and datasets

    Remove computer vision models, augmentations and datasets

    They will be removed in the next release since:

    1. I'm not going to used these methods anytime soon and I don't have the time to test them properly
    2. They are out of scope of the library. It is meant to be used for machine learning techniques, focused on tabular data. In the feature it may be worth considering the development of another library for computer vision, for example.
    3. Setting Pytorch as a dependency for a reduced part of the library isn't particularly efficient.
    wontfix 
    opened by joaopfonseca 0
  • Host all raw data from datasets submodule elsewhere

    Host all raw data from datasets submodule elsewhere

    With Python 3.11, downloading some datasets returns an SSL error (when unsafe legacy renegotiation disabled). It happens when the server doesn't support "RFC 5746 secure renegotiation" and the client is using OpenSSL 3, which enforces that standard by default (source).

    Hosting the raw data elsewhere should fix this issue.

    bug 
    opened by joaopfonseca 0
  • Review and add examples to documentation

    Review and add examples to documentation

    The readthedocs page is getting a bit outdated:

    • [x] Add support for Python 3.10
    • [ ] Add support for Python 3.11
    • [ ] Check for missing, deleted or renamed functions and objects
    • [ ] Review content as a whole
    • [ ] Add examples to documentation
    • [ ] Add dependency groups to documentation
    • [ ] README contains dependencies that will no longer be used
    documentation 
    opened by joaopfonseca 0
Releases(v0.4a2)

  • v0.4a2(Jan 2, 2023)

    NOTE: This pre-release contains implementations of algorithms for Self-supervised learning (BYOL and SimSiam). This release also contains objects to download image data from Pytorch and general definitions for image augmentations. They will be removed in the next release since:

    1. I'm not going to used these methods anytime soon and I don't have the time to test them properly
    2. They are out of scope of the library. It is meant to be used for machine learning techniques, focused on tabular data. In the feature it may be worth considering the development of another library for computer vision, for example.
    3. Setting Pytorch as a dependency for a reduced part of the library isn't particularly efficient.

    Full Changelog: https://github.com/joaopfonseca/ml-research/compare/v0.4a1...v0.4a2

    Source code(tar.gz)
    Source code(zip)

  • v0.4a1(Apr 14, 2022)

  • v0.3.4(Feb 14, 2022)

  • v0.3.3(Feb 14, 2022)

  • v0.3.2(Feb 14, 2022)

  • v0.3.1(Feb 14, 2022)

  • v0.3.0(Feb 14, 2022)

  • v0.2.1(Feb 14, 2022)

  • v0.2.0(Feb 14, 2022)

  • 0.1.0(Feb 14, 2022)

Owner
João Fonseca
PhD student | Researcher | Invited lecturer @ NOVA Information Management School
João Fonseca
GitHub Repository
Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Semi-supervised Deep Kernel Learning This is the code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data

58 Oct 26, 2022
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Feng 2 Nov 19, 2021
Code accompanying paper: Meta-Learning to Improve Pre-Training

Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P

28 Dec 31, 2022
Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Demetri Pananos 9 Oct 04, 2022
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark Yong

19 Dec 17, 2022
Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning

Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning Kajetan Schweighofer1, Markus Hofmarcher1, Marius-Constantin D

Institute for Machine Learning, Johannes Kepler University Linz 17 Dec 28, 2022
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations 3D-aware Image Synthesis via Learning Structural and Textura

GenForce: May Generative Force Be with You 116 Dec 26, 2022
Personalized Federated Learning using Pytorch (pFedMe)

Personalized Federated Learning with Moreau Envelopes (NeurIPS 2020) This repository implements all experiments in the paper Personalized Federated Le

Charlie Dinh 226 Dec 30, 2022
UMich 500-Level Mobile Robotics Course

MOBILE ROBOTICS: METHODS & ALGORITHMS - WINTER 2022 University of Michigan - NA 568/EECS 568/ROB 530 For slides, lecture notes, and example codes, see

393 Dec 29, 2022
Python library for tracking human heads with FLAME (a 3D morphable head model)

Video Head Tracker 3D tracking library for human heads based on FLAME (a 3D morphable head model). The tracking algorithm is inspired by face2face. It

61 Dec 25, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
A Python library created to assist programmers with complex mathematical functions

libmaths libmaths was created not only as a learning experience for me, but as a way to make mathematical models in seconds for Python users using mat

Simple 73 Oct 02, 2022
[ICCV'21] Pri3D: Can 3D Priors Help 2D Representation Learning?

Pri3D: Can 3D Priors Help 2D Representation Learning? [ICCV 2021] Pri3D leverages 3D priors for downstream 2D image understanding tasks: during pre-tr

Ji Hou 124 Jan 06, 2023
Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

MAAD: A Model for Attended Awareness in Driving Install // Datasets // Training // Experiments // Analysis // License Official PyTorch implementation

7 Oct 16, 2022
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models.

Statutory Interpretation Data Set This repository contains the data set created for the following research papers: Savelka, Jaromir, and Kevin D. Ashl

17 Dec 23, 2022
The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

savior是一个能够进行快速集成算法模块并支持高性能部署的轻量开发框架。能够帮助将团队进行快速想法验证（PoC），避免重复的去github上找模型然后复现模型；能够帮助团队将功能进行流程拆解，很方便的提高分布式执行效率；能够有效减少代码冗余，减少不必要负担。

Tao Luo 125 Dec 22, 2022
TensorFlow Implementation of "Show, Attend and Tell"

Show, Attend and Tell Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attent

Yunjey Choi 902 Nov 29, 2022
A playable implementation of Fully Convolutional Networks with Keras.

keras-fcn A re-implementation of Fully Convolutional Networks with Keras Installation Dependencies keras tensorflow Install with pip $ pip install git

JihongJu 202 Sep 07, 2022
Official PyTorch implementation of UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

UACANet: Uncertainty Aware Context Attention for Polyp Segmentation Official pytorch implementation of UACANet: Uncertainty Aware Context Attention fo

Taehun Kim 85 Dec 14, 2022