SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1600 of 3073 papers

TitleStatusHype
Multilabel Classification using Bayesian Compressed Sensing—0
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure—0
Multilinear Hyperplane Hashing—0
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography—0
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach—0
Multi-Objective Bayesian Optimization with Active Preference Learning—0
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes—0
Multiple-Instance Active Learning—0
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus—0
Multi-Task Consistency for Active Learning—0
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning—0
Multi-View Active Learning in the Non-Realizable Case—0
MuRAL: Multi-Scale Region-based Active Learning for Object Detection—0
Narrowing the Loop: Integration of Resources and Linguistic Dataset Development with Interactive Machine Learning—0
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting—0
Near Optimal Bayesian Active Learning for Decision Making—0
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests—0
Near-Optimal Bayesian Active Learning with Noisy Observations—0
Near-optimal inference in adaptive linear regression—0
Near-optimality for infinite-horizon restless bandits with many arms—0
Needle in a Haystack: Reducing the Costs of Annotating Rare-Class Instances in Imbalanced Datasets—0
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials—0
Neural Active Learning Beyond Bandits—0
Neural Active Learning Meets the Partial Monitoring Framework—0
Neural Active Learning with Performance Guarantees—0
Neural Network-Based Active Learning in Multivariate Calibration—0
Neural Window Decoder for SC-LDPC Codes—0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning—0
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels—0
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition—0
Noise-tolerant, Reliable Active Classification with Comparison Queries—0
Noisy Generalized Binary Search—0
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity—0
Nonparametric active learning for cost-sensitive classification—0
Nonparametric adaptive active learning under local smoothness condition—0
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning—0
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage—0
Nuances in Margin Conditions Determine Gains in Active Learning—0
Nuclear Discrepancy for Active Learning—0
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—0
OASIS: An Active Framework for Set Inversion—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets—0
Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks—0
On Active Learning for Gaussian Process-based Global Sensitivity Analysis—0
On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified