165 Open source projects that are alternatives of or similar to JCLAL

Semi-Supervised Learning and Collective Classification

Active learning for deep object detection using YOLO

active learning for accelerated high-throughput virtual screening

TensorFlow implementation for "Guided Optical Flow Learning"

A WEKA package for analyzing emotion and sentiment of tweets.

JuliaLang version of "An Introduction to Statistical Learning: With Applications in R"

tools for fast reading of docs

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]

Reproduce some methods in semi-supervised papers.

PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

Arrhythmia Classification through Characteristics Extraction with Discrete Wavelet Transform & Machine Learning

Python 3 wrapper for Weka using python-javabridge.

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

Univariate and multivariate time series forecasting

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. (DEPRECATED)

Lecture Slides for Introduction to Data Science

An interactive dashboard for visualisation, integration and classification of data using Active Learning.

🎀 A data mining suite for gene expression data.

Yum-me is a nutrient based food recommendation system

Matlab library for gradient descent algorithms: Version 1.0.1

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"

Implementing Billion-scale semi-supervised learning for image classification using Pytorch

Statistical Models with Regularization in Pure Julia

Invariant representation learning from imaging and spectral data

Tensorflow Implementation on Paper [ECCV2018]Semi-Supervised Deep Learning with Memory

Generalized Product Quantization Network For Semi-supervised Image Retrieval - CVPR 2020

Benchmarks for semi-supervised domain generalization.

PyTorch implementation of EMAN for self-supervised and semi-supervised learning: https://arxiv.org/abs/2101.08482

EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs (AAAI 2021)

Code for the paper: "SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification"

Tornado is a human-in-the-loop machine learning framework that helps you exploit your unlabelled data to train models through a simple and easy to use web interface.

Tools for detecting wildlife in aerial images using active learning

Semisupervided LDA for theory-driven text analysis

Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 paper: https://arxiv.org/abs/2102.08622.

A set of tools for leveraging pre-trained embeddings, active learning and model explainability for effecient document classification

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE

Python 2.7 wrapper for Weka using javabridge.

Joint Semi-supervised Learning of Image Registration and Segmentation

Keras implementation of temporal ensembling(semi-supervised learning)

Enhancing the BERT training with Semi-supervised Generative Adversarial Networks

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Arrhythmia Classification through Characteristics Extraction with Discrete Wavelet Transform & WEKA/MATLAB Supervised Training

Deep Metric Transfer for Label Propagation with Limited Annotated Data

This project is a semi-supervised approach to detect emotions on faces in-the-wild using GAN

Python codes for weakly-supervised learning

BoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.

[CVPR 2022] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

Active Learning in R

Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

A question answering system which utilises machine learning.

Pytorch Implementation of HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning (https://arxiv.org/abs/1807.11407)

Advanced Normalization Tools in R

Python module for data scientists for quick creating annotation projects.

No description or website provided.

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training