556 skills found · Page 3 of 19
som-shahlab / TroveWeakly supervised medical named entity classification
ki-ljl / PyG GCNPyG implementation of GCN (Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017).Datasets: CiteSeer, Cora, PubMed, NELL.
dlcjfgmlnasa / NeuroNet[Arxiv] NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
miccaiif / WENOOfficial PyTorch implementation of our NeurIPS 2022 paper: Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
koheiw / NewsmapSemi-supervised algorithm for geographical document classification
zijian-hu / SimPLECode for the paper: "SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification"
JeanBertinR / ShinyMLshinyML is a R package that use h2o and Spark frameworks to easily compare supervised machine learning models on regression or classification tasks (published on CRAN in July 2019)
Yochengliu / MLIC KD WSDMulti-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection (ACM MM 2018)
lpfgarcia / UcippUCI++: A huge collection of preprocessed datasets for supervised classification problems in ARFF format
clips / TopboxPython 2 & 3 wrapper around the Stanford Topic Modeling Toolbox. Intended to be used for hassle-free supervised topic classification with Labeled Latent Dirichlet Allocation (L-LDA, LLDA, sLDA).
victorchen96 / ReNodeCode for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".
reddyprasade / Machine Learning With Scikit Learn Python 3.xIn general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
theodoriss / Gcn DemoSemi-Supervised classification with Graph Convolution Networks using Pytorch
ShannonAI / Neural Semi Supervised Learning For Text ClassificationSemi-supervised Learning for Sentiment Analysis
maxwell0027 / PEFAT[CVPR2023]PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training
geojames / CNN Supervised ClassificationPython code for self-supervised classification of remotely sensed imagery - part of the Deep Riverscapes project
opetrova / SemiSupervisedPytorchGANA semi supervised GAN for image classification implemented in Pytorch
dheeraj7596 / ConWeaCode for the paper "Contextualized Weak Supervision for Text Classification"
Closed11 / Unsupervised Image ClassificationA very simple self-supervised image classification framework!
ruidan / DASCode and datasets for EMNLP2018 paper ‘‘Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification’’.
