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
This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation.

ISL This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation, which is accepted

19 May 04, 2022
PyTorch implementation of PNASNet-5 on ImageNet

PNASNet.pytorch PyTorch implementation of PNASNet-5. Specifically, PyTorch code from this repository is adapted to completely match both my implemetat

Chenxi Liu 314 Nov 25, 2022
Dyalog-apl-docset - Dyalog APL Dash Docset Generator

Dyalog APL Dash Docset Generator o alasa e kili sona kepeken tenpo lili a A Dash

Maciej Goszczycki 1 Jan 10, 2022
[ICLR'21] Counterfactual Generative Networks

This repository contains the code for the ICLR 2021 paper "Counterfactual Generative Networks" by Axel Sauer and Andreas Geiger. If you want to take the CGN for a spin and generate counterfactual ima

88 Jan 02, 2023
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage

Keepsake Version control for machine learning. Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Goo

Replicate 1.6k Dec 29, 2022
The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

TwoStageAlign The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift Pa

Shi Guo 32 Dec 15, 2022
Garbage classification using structure data.

垃圾分类模型使用说明 1.包含以下数据文件 文件 描述 data/MaterialMapping.csv 物体以及其归类的信息 data/TestRecords 光谱原始测试数据 CSV 文件 data/TestRecordDesc.zip CSV 文件描述文件 data/Boundaries.cs

wenqi 1 Dec 10, 2021
PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Study-CSRNet-pytorch This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

0 Mar 01, 2022
Fast and robust certifiable relative pose estimation

Fast and Robust Relative Pose Estimation for Calibrated Cameras This repository contains the code for the relative pose estimation between two central

42 Dec 06, 2022
Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time".

FastBERT Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time". Good News 2021/10/29 - Code: Code of FastPLM is released on

Weijie Liu 584 Jan 02, 2023
Patches desktop steam to look like the new steamdeck ui.

steam_deck_ui_patch The Deck UI patch will patch the regular desktop steam to look like the brand new SteamDeck UI. This patch tool currently works on

The_IT_Dude 3 Aug 29, 2022
An educational tool to introduce AI planning concepts using mobile manipulator robots.

JEDAI Explains Decision-Making AI Virtual Machine Image The recommended way of using JEDAI is to use pre-configured Virtual Machine image that is avai

Autonomous Agents and Intelligent Robots 13 Nov 15, 2022
Dynamic Bottleneck for Robust Self-Supervised Exploration

Dynamic Bottleneck Introduction This is a TensorFlow based implementation for our paper on "Dynamic Bottleneck for Robust Self-Supervised Exploration"

Bai Chenjia 4 Nov 14, 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da

Sanyam Kapoor 18 Nov 17, 2022
An implementation of the 1. Parallel, 2. Streaming, 3. Randomized SVD using MPI4Py

PYPARSVD This implementation allows for a singular value decomposition which is: Distributed using MPI4Py Streaming - data can be shown in batches to

Romit Maulik 44 Dec 31, 2022
Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"

One2Set This repository contains the code for our ACL 2021 paper “One2Set: Generating Diverse Keyphrases as a Set”. Our implementation is built on the

Jiacheng Ye 63 Jan 05, 2023
The author's officially unofficial PyTorch BigGAN implementation.

BigGAN-PyTorch The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Sc

Andy Brock 2.6k Jan 02, 2023
Its a Plant Leaf Disease Detection System based on Machine Learning.

My_Project_Code Its a Plant Leaf Disease Detection System based on Machine Learning. I have used Tomato Leaves Dataset from kaggle. This system detect

Sanskriti Sidola 3 Jun 15, 2022
SimulLR - PyTorch Implementation of SimulLR

PyTorch Implementation of SimulLR There is an interesting work[1] about simultan

11 Dec 22, 2022