https://github.com/SatyadevNtv/ROT4C
공식 코드Efficient Robust Optimal Transport with Application to Multi-Label Classification
Papers with Code 아카이브
Multi-class Classification
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Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Shaul1321/nullspace_projection
공식 코드Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
https://github.com/The-FinAI/FinTagging
공식 코드FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial Information
https://github.com/TheAdamG/Gradual-AACBR
공식 코드https://github.com/TianXieUSC/GraphHop
공식 코드GraphHop: An Enhanced Label Propagation Method for Node Classification
https://github.com/UCLA-StarAI/Semantic-Loss
공식 코드A Semantic Loss Function for Deep Learning with Symbolic Knowledge
https://github.com/YBZh/MultiClassDA
공식 코드Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
https://github.com/Yufang-Liu/category-generator
공식 코드https://github.com/ads-ai/tagrec_plus_plus_tkde
공식 코드TagRec++: Hierarchical Label Aware Attention Network for Question Categorization
https://github.com/ai-growth-lab/semp_patent
공식 코드A Survey on Sentence Embedding Models Performance for Patent Analysis
https://github.com/ai-thpremed/tabkanet
공식 코드TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
https://github.com/ai4ce/Contrastive-SPARE3D
공식 코드Self-supervised Spatial Reasoning on Multi-View Line Drawings
https://github.com/ai4life-group/average-case-robustness
공식 코드Characterizing Data Point Vulnerability via Average-Case Robustness
https://github.com/ai4protein/venusx
공식 코드VenusX: Unlocking Fine-Grained Functional Understanding of Proteins
https://github.com/alexnowakvila/maxminloss
공식 코드Consistent Structured Prediction with Max-Min Margin Markov Networks
https://github.com/ameliajimenez/curriculum-learning-prior-uncertainty
공식 코드https://github.com/andreanne-lemay/gray_zone_assessment
공식 코드Improving the repeatability of deep learning models with Monte Carlo dropout
https://github.com/andreasgrv/sigmoid-bottleneck
공식 코드Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification
https://github.com/ankur-samanta/mvmtnet-transformer_ecgclassification
공식 코드https://github.com/armiro/COVID-CXNet
공식 코드COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning
https://github.com/arnavs04/capsule-vision-2024
공식 코드Multi-Class Abnormality Classification in Video Capsule Endoscopy Using Deep Learning
https://github.com/atschalz/mcgmenn
공식 코드Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods
https://github.com/awsaf49/artifact
공식 코드https://github.com/ayushroy2001/fa-net
공식 코드FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays
https://github.com/bat-sheva/conformal-learning
공식 코드Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
https://github.com/baumanndominik/cme_based_classification_bounds
공식 코드https://github.com/bcsaldias/yes-no-crowdsourcing
공식 코드A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification
https://github.com/berryweinst/pairwise-margin-maximization
공식 코드https://github.com/bikash617/Aggressive-Sampling-for-Multi-class-to-BinaryReduction
공식 코드Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
https://github.com/biomedsciai/gene-benchmark
공식 코드Does your model understand genes? A benchmark of gene properties for biological and text models
https://github.com/cbellei/LabelGCN
공식 코드Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks
https://github.com/chengjin-git/hmil
공식 코드HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
https://github.com/chenpf1025/RobustnessAccuracy
공식 코드Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
https://github.com/chuangua/contrastivelossmlml
공식 코드Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
https://github.com/cipriantruica/misrobaerta_transformers-vs-misinformation
공식 코드https://github.com/cis-ncbj/covid19-stylegan-augmentation
공식 코드Performance of GAN-based augmentation for deep learning COVID-19 image classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.