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 1401–1450 of 3073 papers

TitleStatusHype
Diverse mini-batch Active Learning—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
Diverse Complexity Measures for Dataset Curation in Self-driving—0
DroidStar: Callback Typestates for Android Classes—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
Active Learning in Noisy Conditions for Spoken Language Understanding—0
Distribution-Dependent Sample Complexity of Large Margin Learning—0
An Active Learning Framework for Efficient Robust Policy Search—0
Dual Adversarial Network for Deep Active Learning—0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning—0
Distribution Aware Active Learning—0
DutchSemCor: Targeting the ideal sense-tagged corpus—0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice—0
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling—0
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning—0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms—0
Easy Questions First? A Case Study on Curriculum Learning for Question Answering—0
ED2: Two-stage Active Learning for Error Detection -- Technical Report—0
Distributional Term Set Expansion—0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Information-Theoretic Active Correlation Clustering—0
Effective Data Selection for Seismic Interpretation through Disagreement—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
Effective Version Space Reduction for Convolutional Neural Networks—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Active Community Detection with Maximal Expected Model Change—0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach—0
Efficient Active Learning of Halfspaces: an Aggressive Approach—0
Efficient active learning of sparse halfspaces—0
Efficient active learning of sparse halfspaces with arbitrary bounded noise—0
Efficient Active Learning with Abstention—0
ACIL: Active Class Incremental Learning for Image Classification—0
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs—0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach—0
Efficient Biological Data Acquisition through Inference Set Design—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Distilling the Posterior in Bayesian Neural Networks—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Efficient Deconvolution in Populational Inverse Problems—0
Distance-Penalized Active Learning Using Quantile Search—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