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 2351–2375 of 3073 papers

TitleStatusHype
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Localization-Aware Active Learning for Object Detection—0
Localized active learning of Gaussian process state space models—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover—0
Lower Bounds for Passive and Active Learning—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Low-Regret Active learning—0
Low-Resolution Face Recognition In Resource-Constrained Environments—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment—0
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation—0
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition—0
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
Machine Learning for Molecular Dynamics on Long Timescales—0
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations—0
Maestro: A Gamified Platform for Teaching AI Robustness—0
Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control—0
Making Efficient Use of a Domain Expert's Time in Relation Extraction—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