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 501–550 of 3073 papers

TitleStatusHype
Superposition through Active Learning lens—0
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure—0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Active learning of neural population dynamics using two-photon holographic optogenetics—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
PAL -- Parallel active learning for machine-learned potentialsCode0
Neural Window Decoder for SC-LDPC Codes—0
Active partitioning: inverting the paradigm of active learning—0
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System—0
Maximally Separated Active Learning—0
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
Benchmarking Active Learning for NILM—0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
Influence functions and regularity tangents for efficient active learning—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties—0
Stream-Based Active Learning for Process Monitoring—0
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation spaceCode0
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect EstimationCode0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—0
Targeting Negative Flips in Active Learning using Validation SetsCode0
Learning Quantitative Automata Modulo Theories—0
Deep Active Learning in the Open World—0
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise—0
FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher InformationCode0
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale—0
Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop—0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
An information-matching approach to optimal experimental design and active learning—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal SystemsCode0
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration—0
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry—0
DISCERN: Decoding Systematic Errors in Natural Language for Text ClassifiersCode0
Active Learning for Vision-Language Models—0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Efficient Biological Data Acquisition through Inference Set Design—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
regAL: Python Package for Active Learning of Regression Problems—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Deep Active Learning with Manifold-preserving Trajectory Sampling—0
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency—0
Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents—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