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 1751–1800 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
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization—0
RadGrad: Active learning with loss gradients—0
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data—0
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores—0
Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation—0
Rare event estimation using stochastic spectral embedding—0
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape—0
Ready Policy One: World Building Through Active Learning—0
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming—0
Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution—0
Active learning with binary models for real time data labelling—0
Rebuilding Trust in Active Learning with Actionable Metrics—0
Reciprocal Learning—0
Recognition of Mental Adjectives in An Efficient and Automatic Style—0
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles—0
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models—0
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization—0
Reduced-order structure-property linkages for stochastic metamaterials—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Reducing Confusion in Active Learning for Part-Of-Speech Tagging—0
Reducing Label Effort: Self-Supervised meets Active Learning—0
Refined Error Bounds for Several Learning Algorithms—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