SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6026–6050 of 14581 papers

TitleStatusHype
UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models—0
ULisboa: Identification and Classification of Medical Concepts—0
Ultra-Fast Shapelets for Time Series Classification—0
Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics—0
UMCC\_DLSI: Semantic and Lexical features for detection and classification Drugs in biomedical texts—0
Unary and Binary Classification Approaches and their Implications for Authorship Verification—0
Unbiased Loss Functions for Extreme Classification With Missing Labels—0
Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms—0
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language—0
Unbounded Output Networks for Classification—0
Uncertainty-aware Active Learning for Optimal Bayesian Classifier—0
Uncertainty-Aware Few-Shot Image Classification—0
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy—0
Uncertainty-aware Self-training for Few-shot Text Classification—0
Uncertainty-aware Self-training for Text Classification with Few Labels—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images—0
Uncertainty Gated Network for Land Cover Segmentation—0
Uncertainty in Gradient Boosting via Ensembles—0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference—0
Uncertainty in the Variational Information Bottleneck—0
A Simple Framework to Quantify Different Types of Uncertainty in Deep Neural Networks for Image Classification—0
Uncertainty quantification in graph-based classification of high dimensional data—0
Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation—0
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All datasetsXORirisActivityFashion-MNISTISOLETMice ProteinMNISTWine

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1KNNAccuracy93.1—Unverified
2RFAccuracy92.96—Unverified
3CARTAccuracy92.82—Unverified
4LDAAccuracy77.22—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified
2XBNETAccuracy100—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy42—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy67.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy68.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy13.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy90.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified