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
Employs neural networks to classify images into four categories: ship, automobile, dog or frog

Neural Net Image Classifier Employs neural networks to classify images into four categories: ship, automobile, dog or frog Viterbi_1.py uses a classic

Riley Baker 1 Jan 18, 2022
This is a collection of our NAS and Vision Transformer work.

This is a collection of our NAS and Vision Transformer work.

Microsoft 828 Dec 28, 2022
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"

L2F - Learning to Forget for Meta-Learning Sungyong Baik, Seokil Hong, Kyoung Mu Lee Source code for CVPR 2020 paper "Learning to Forget for Meta-Lear

Sungyong Baik 29 May 22, 2022
GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms Trying to publish a new machine learning model and can't write a decent title for your pa

264 Nov 08, 2022
Official Repository of NeurIPS2021 paper: PTR

PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning Figure 1. Dataset Overview. Introduction A critical aspect of human vis

Yining Hong 32 Jun 02, 2022
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
A Runtime method overload decorator which should behave like a compiled language

strongtyping-pyoverload A Runtime method overload decorator which should behave like a compiled language there is a override decorator from typing whi

20 Oct 31, 2022
Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

ForecastingNonverbalSignals This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative A

1 Feb 10, 2022
3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

3rd Place Solution of Traffic4Cast 2021 Core Challenge This is the code for our solution to the NeurIPS 2021 Traffic4Cast Core Challenge. Paper Our so

7 Jul 25, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
Pmapper is a super-resolution and deconvolution toolkit for python 3.6+

pmapper pmapper is a super-resolution and deconvolution toolkit for python 3.6+. PMAP stands for Poisson Maximum A-Posteriori, a highly flexible and a

NASA Jet Propulsion Laboratory 8 Nov 06, 2022
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks

SalFBNet This repository includes Pytorch implementation for the following paper: SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolu

12 Aug 12, 2022
Python wrapper to access the amazon selling partner API

PYTHON-AMAZON-SP-API Amazon Selling-Partner API If you have questions, please join on slack Contributions very welcome! Installation pip install pytho

Michael Primke 330 Jan 06, 2023
🐦 Opytimizer is a Python library consisting of meta-heuristic optimization techniques.

Opytimizer: A Nature-Inspired Python Optimizer Welcome to Opytimizer. Did you ever reach a bottleneck in your computational experiments? Are you tired

Gustavo Rosa 546 Dec 31, 2022
Use evolutionary algorithms instead of gridsearch in scikit-learn

sklearn-deap Use evolutionary algorithms instead of gridsearch in scikit-learn. This allows you to reduce the time required to find the best parameter

rsteca 709 Jan 03, 2023
A curated list of awesome Active Learning

Awesome Active Learning 🤩 A curated list of awesome Active Learning ! 🤩 Background (image source: Settles, Burr) What is Active Learning? Active lea

BAI Fan 431 Jan 03, 2023
Public Implementation of ChIRo from "Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations"

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations This directory contains the model architectures and experimental

35 Dec 05, 2022
Age and Gender prediction using Keras

cnn_age_gender Age and Gender prediction using Keras Dataset example : Description : UTKFace dataset is a large-scale face dataset with long age span

XN3UR0N 58 May 03, 2022
This repository contains code from the paper "TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network"

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network This repository contains code from the paper "TTS-GAN: A Transformer-based Tim

Intelligent Multimodal Computing and Sensing Laboratory (IMICS Lab) - Texas State University 108 Dec 29, 2022