Multi-class Classification
279 papers with code • 5 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in Multi-class Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Multi-class Classification models and implementationsDatasets
SMM4H
TII-SSRC-23
SF-MASK
CVE
Most implemented papers
SentEval: An Evaluation Toolkit for Universal Sentence Representations
facebookresearch/SentEval
•
•
LREC 2018
We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.
Evidential Deep Learning to Quantify Classification Uncertainty
labmlai/annotated_deep_learning_paper_implementations
•
•
NeurIPS 2018
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems.
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Demfier/multimodal-speech-emotion-recognition
•
•
In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition.
Efficient Deep Learning for Stereo Matching
saakuraa/cvpr16_stereo_public
•
•
CVPR 2016
In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation.
Efficient Set-Valued Prediction in Multi-Class Classification
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee.
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.
Network Representation Learning with Rich Text Information
Representation learning has shown its effectiveness in many tasks such as image classification and text mining.
GenSVM: A Generalized Multiclass Support Vector Machine
Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem.
HDLTex: Hierarchical Deep Learning for Text Classification
kk7nc/HDLTex
•
•
This is because along with this growth in the number of documents has come an increase in the number of categories.
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Dichoto/LGL-INR
•
•
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision.
