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 2001–2050 of 3073 papers

TitleStatusHype
Exploring and Addressing Reward Confusion in Offline Preference Learning—0
Exploring Connections Between Active Learning and Model Extraction—0
Exploring Label Dependency in Active Learning for Phenotype Mapping—0
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers—0
Exploring Representativeness and Informativeness for Active Learning—0
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation—0
Exponential Savings in Agnostic Active Learning through Abstention—0
Exponentiated Gradient Exploration for Active Learning—0
Extensions of Generalized Binary Search to Group Identification and Exponential Costs—0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT—0
Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting—0
Factored Contextual Policy Search with Bayesian Optimization—0
Factored Contextual Policy Search with Bayesian Optimization—0
Failure-averse Active Learning for Physics-constrained Systems—0
Fair Active Learning in Low-Data Regimes—0
Fairness-Aware Data Valuation for Supervised Learning—0
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring—0
Fair Robust Active Learning by Joint Inconsistency—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning—0
Fast active learning for pure exploration in reinforcement learning—0
Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)—0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Fast Design Space Exploration of Nonlinear Systems: Part II—0
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training—0
Fast Interactive Image Retrieval using large-scale unlabeled data—0
Fast kNN mode seeking clustering applied to active learning—0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning—0
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials—0
FDive: Learning Relevance Models using Pattern-based Similarity Measures—0
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification—0
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems—0
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
Few-Shot Continual Active Learning by a Robot—0
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts—0
Fine-Grained Product Class Recognition for Assisted Shopping—0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression—0
FITAnnotator: A Flexible and Intelligent Text Annotation System—0
Flattening a Hierarchical Clustering through Active Learning—0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what—0
Focused Active Learning for Histopathological Image Classification—0
Focusing Annotation for Semantic Role Labeling—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