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 1751–1775 of 3073 papers

TitleStatusHype
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography—0
Probabilistic Deep Learning for Instance Segmentation—0
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data—0
Probabilistic Model-Agnostic Meta-Learning—0
Probability trees and the value of a single intervention—0
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory—0
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method—0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
Projection based Active Gaussian Process Regression for Pareto Front Modeling—0
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design—0
Provably Accurate Shapley Value Estimation via Leverage Score Sampling—0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
Pseudo-triplet Guided Few-shot Composed Image Retrieval—0
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play—0
QActor: On-line Active Learning for Noisy Labeled Stream Data—0
QBDC: Query by dropout committee for training deep supervised architecture—0
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing—0
Quantifying Policy Administration Cost in an Active Learning Framework—0
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data—0
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems—0
Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis—0
Query-Efficient Black-Box Attack by Active Learning—0
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning—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