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 1351–1400 of 3073 papers

TitleStatusHype
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
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 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
Bucketized Active Sampling for Learning ACOPF—0
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning—0
Continuous Active Learning Using Pretrained Transformers—0
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores—0
Multi-task Active Learning for Pre-trained Transformer-based ModelsCode0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Active Learning for Non-Parametric Choice Models—0
Deep Surrogate of Modular Multi Pump using Active Learning—0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
Active Learning on a Programmable Photonic Quantum Processor—0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic QuantitiesCode0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging—0
Deep Active Learning with Budget Annotation—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Unsupervised Frequent Pattern Mining for CEP—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Partial-Monotone Adaptive Submodular Maximization—0
Efficient Classification with Counterfactual Reasoning and Active LearningCode0
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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