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 1926–1950 of 3073 papers

TitleStatusHype
Social Media Predictive Analytics—0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
Solving Multi-Arm Bandit Using a Few Bits of Communication—0
Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction—0
Sophisticated Inference—0
SoQal: Selective Oracle Questioning in Active Learning—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Spatially regularized active diffusion learning for high-dimensional images—0
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning—0
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry—0
Sprucing up the trees -- Error detection in treebanks—0
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions—0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning—0
STARdom: an architecture for trusted and secure human-centered manufacturing systems—0
Statistical Active Learning Algorithms—0
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy—0
Statistical Hardware Design With Multi-model Active Learning—0
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains—0
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning—0
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Stochastic Descent Analysis of Representation Learning Algorithms—0
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic—0
Stochastic Submodular Maximization via Polynomial Estimators—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