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
Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)

Compressive Visual Representations This repository contains the source code for our paper, Compressive Visual Representations. We developed informatio

Google Research 30 Nov 23, 2022
Project ArXiv Citation Network

Project ArXiv Citation Network Overview This project involved the analysis of the ArXiv citation network. Usage The complete code of this project is i

Dennis Núñez-Fernández 5 Oct 20, 2022
Graph Convolutional Networks in PyTorch

Graph Convolutional Networks in PyTorch PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1]. For a hi

Thomas Kipf 4.5k Dec 31, 2022
An official implementation of "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation" (CVPR 2021) in PyTorch.

BANA This is the implementation of the paper "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation". For more inf

CV Lab @ Yonsei University 59 Dec 12, 2022
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision Project | PDF | Poster Fangyu Li, N. Dinesh Reddy, X

25 Dec 21, 2022
Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

ProxyFL Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing" Authors: Shivam Kalra*, Junfeng Wen*, Jess

Layer6 Labs 14 Dec 06, 2022
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing

Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing Paper Introduction Multi-task indoor scene understanding is widely considered a

62 Dec 05, 2022
Pytorch implementation of the unsupervised object discovery method LOST.

LOST Pytorch implementation of the unsupervised object discovery method LOST. More details can be found in the paper: Localizing Objects with Self-Sup

Valeo.ai 189 Dec 25, 2022
Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Wav2CLIP 🚧 WIP 🚧 Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP 📄 🔗 Ho-Hsiang Wu, Prem Seetharaman

Descript 240 Dec 13, 2022
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising By Tengfei Liang, Yi Jin, Yidong Li, Tao Wang. Th

workingcoder 115 Jan 05, 2023
Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)

Unsupervised Attributed Multiplex Network Embedding (DMGI) Overview Nodes in a multiplex network are connected by multiple types of relations. However

Chanyoung Park 114 Dec 06, 2022
[ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

F8Net Fixed-Point 8-bit Only Multiplication for Network Quantization (ICLR 2022 Oral) OpenReview | arXiv | PDF | Model Zoo | BibTex PyTorch implementa

Snap Research 76 Dec 13, 2022
Code and training data for our ECCV 2016 paper on Unsupervised Learning

Shuffle and Learn (Shuffle Tuple) Created by Ishan Misra Based on the ECCV 2016 Paper - "Shuffle and Learn: Unsupervised Learning using Temporal Order

Ishan Misra 44 Dec 08, 2021
An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects different compression algorithms have.

ImageCompressionSimulation An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects o

James Park 1 Dec 11, 2021
PyTorch ,ONNX and TensorRT implementation of YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

4.2k Jan 01, 2023
Code for the Paper "Diffusion Models for Handwriting Generation"

Code for the Paper "Diffusion Models for Handwriting Generation"

62 Dec 21, 2022
Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21

MonoFlex Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21. Work in progress. Installation This repo is tested w

Yunpeng 169 Dec 06, 2022
Differentiable Optimizers with Perturbations in Pytorch

Differentiable Optimizers with Perturbations in PyTorch This contains a PyTorch implementation of Differentiable Optimizers with Perturbations in Tens

Jake Tuero 54 Jun 22, 2022
An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

Deep-motion-editing This library provides fundamental and advanced functions to work with 3D character animation in deep learning with Pytorch. The co

1.2k Dec 29, 2022
Code for the paper "Adapting Monolingual Models: Data can be Scarce when Language Similarity is High"

Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

Wietse de Vries 5 Aug 02, 2021