SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 11651–11700 of 14581 papers

TitleStatusHype
Interactive algorithms: from pool to stream—0
Interactive Graphics for Visually Diagnosing Forest Classifiers in R—0
InterActive: Inter-Layer Activeness Propagation—0
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness—0
Interactive multiclass segmentation using superpixel classification—0
Interactive Naming for Explaining Deep Neural Networks: A Formative Study—0
Interactive Semantic Featuring for Text Classification—0
Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective—0
Interference Classification Using Deep Neural Networks—0
IC-Network: Efficient Structure for Convolutional Neural Networks—0
Internal Guidance for Satallax—0
Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO—0
Interpolated Discretized Embedding of Single Vectors and Vector Pairs for Classification, Metric Learning and Distance Approximation—0
Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network—0
Interpretability Beyond Classification Output: Semantic Bottleneck Networks—0
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
Interpretable Attention Guided Network for Fine-grained Visual Classification—0
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study—0
Interpretable Classification Models for Recidivism Prediction—0
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques—0
Interpretable Classification via Supervised Variational Autoencoders and Differentiable Decision Trees—0
Interpretable CNNs for Object Classification—0
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint—0
Interpretable Deep Learning applied to Plant Stress Phenotyping—0
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra—0
Interpretable Deep Neural Networks for Single-Trial EEG Classification—0
Interpretable Discovery in Large Image Data Sets—0
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia—0
Interpretable Low-Rank Document Representations with Label-Dependent Sparsity Patterns—0
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications—0
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder—0
Interpretable Phase Detection and Classification with Persistent Homology—0
Interpretable Rationale Augmented Charge Prediction System—0
Interpretable Text Classification Using CNN and Max-pooling—0
Interpretable Two-level Boolean Rule Learning for Classification—0
Interpretable Two-level Boolean Rule Learning for Classification—0
Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Nodes—0
Intentional Attention Mask Transformation for Robust CNN Classification—0
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results—0
Interpretation of Prediction Models Using the Input Gradient—0
Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields—0
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses—0
Interpreting Classifiers through Attribute Interactions in Datasets—0
Interpreting Deep Classification Models With Bayesian Inference—0
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification—0
Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI—0
Interpreting Finite Automata for Sequential Data—0
Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis—0
Interpreting random forest classification models using a feature contribution method—0
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation—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