https://github.com/heimine/nc_mlab
공식 코드Neural Collapse in Multi-label Learning with Pick-all-label Loss
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/heimine/pnc_dln
공식 코드Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
https://github.com/hongbo-z/snna
공식 코드https://github.com/hope-rita/patcls
공식 코드Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
https://github.com/hsianghsu/fair-projection
공식 코드https://github.com/hugo101/hyperevidentialnn
공식 코드Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
https://github.com/humaticslab/sf-mask
공식 코드A Masked Face Classification Benchmark on Low-Resolution Surveillance Images
https://github.com/idiap/speech-utility-bioacoustics
공식 코드On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis
https://github.com/iesl/metanlp
공식 코드Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks
https://github.com/intelligence-csd-auth-gr/LionLearn/tree/master/LionForests
공식 코드https://github.com/istiakshihab/automated-retail-checkout-aicity22
공식 코드https://github.com/iupui-soic/radiology_fsl
공식 코드Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets
https://github.com/jej127/bias-experts
공식 코드Improving Bias Mitigation through Bias Experts in Natural Language Understanding
https://github.com/jfc43/advex
공식 코드Concise Explanations of Neural Networks using Adversarial Training
https://github.com/jiehsheng/PatentBERT
공식 코드PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model
https://github.com/jintangxue/wordfs
공식 코드Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection
https://github.com/jkrishnan95v/Signal_detector
공식 코드https://github.com/jmbuena/toolbox.badacost.public
공식 코드https://github.com/joanagoncalveslab/DCAST
공식 코드DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning
https://github.com/josephsdavid/semg_repro
공식 코드https://github.com/jsb-ucla/itca
공식 코드https://github.com/juliameister/dl-confident-loss-function
공식 코드A novel Deep Learning approach for one-step Conformal Prediction approximation
https://github.com/junqi-jiang/interval-abstractions
공식 코드Interval Abstractions for Robust Counterfactual Explanations
https://github.com/kahramankostas/IoTDevID
공식 코드IoTDevID: A Behavior-Based Device Identification Method for the IoT
https://github.com/kahramankostas/iotdevidv2
공식 코드IoTDevID: A Behavior-Based Device Identification Method for the IoT
https://github.com/kancharlavamshi/Spatial-encoding-of-BOLD-fmri-time-series-for-categorical-static-images-across-visual-dataset
공식 코드https://github.com/kartikeypant/smokeng-tobacco-classification
공식 코드SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text
https://github.com/kittykg/neural-dnf-cub
공식 코드Neuro-symbolic Rule Learning in Real-world Classification Tasks
https://github.com/kittykg/neural-dnf-tmc
공식 코드Neuro-symbolic Rule Learning in Real-world Classification Tasks
https://github.com/kk7nc/HDLTex
공식 코드https://github.com/kun0906/flr
공식 코드Financial Data Analysis with Robust Federated Logistic Regression
https://github.com/leishida/Um-Classification
공식 코드Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
https://github.com/lejon/DiagonalOrthantLDA
공식 코드DOLDA - a regularized supervised topic model for high-dimensional multi-class regression
https://github.com/ljw9510/smf
공식 코드Exponentially Convergent Algorithms for Supervised Matrix Factorization
https://github.com/luisoala/non-iid-ssdl
공식 코드https://github.com/luka-group/NLI_as_Indirect_Supervision
공식 코드Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.