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 1151–1200 of 3073 papers

TitleStatusHype
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures—0
Active Learning for Regression with Aggregated Outputs—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Robust Active Distillation—0
Nonstationary data stream classification with online active learning and siamese neural networksCode0
Improved Algorithms for Neural Active LearningCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
Improving Generative Flow Networks with Path Regularization—0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
From Weakly Supervised Learning to Active Learning—0
Smart Active Sampling to enhance Quality Assurance Efficiency—0
A Bibliographic View on Constrained ClusteringCode0
Fair Robust Active Learning by Joint Inconsistency—0
Active Keyword Selection to Track Evolving Topics on TwitterCode0
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair ClusteringCode0
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active LearningCode0
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation—0
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography—0
Predictive Scale-Bridging Simulations through Active Learning—0
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Introspective Learning : A Two-Stage Approach for Inference in Neural NetworksCode0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—0
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation—0
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace ApplicationsCode0
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning—0
Boosting Robustness Verification of Semantic Feature Neighborhoods—0
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing—0
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Learning of Classifiers with Label and Seed Queries—0
Peer to Peer Learning Platform Optimized With Machine Learning—0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Domain Adaptation from ScratchCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Confidence Estimation for Object Detection in Document Images—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Open Long-Tailed Recognition in a Dynamic World—0
Semi-supervised Learning with Deterministic Labeling and Large Margin ProjectionCode0
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning—0
Bucketized Active Sampling for Learning ACOPF—0
Continuous Active Learning Using Pretrained Transformers—0
BenchPress: A Deep Active Benchmark GeneratorCode1
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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