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 751–800 of 3073 papers

TitleStatusHype
Few-shot Named Entity Recognition via Superposition Concept DiscriminationCode0
Enhancing Semi-supervised Domain Adaptation via Effective Target LabelingCode0
An active learning model to classify animal species in Hong Kong—0
On the Fragility of Active Learners for Text ClassificationCode0
Generative Active Learning for Image Synthesis PersonalizationCode0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project—0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
Deep Active Learning: A Reality Check—0
Annotation-Efficient Polyp Segmentation via Active Learning—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations—0
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active LearningCode0
Deep Submodular Peripteral Networks—0
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement—0
Active Generation for Image ClassificationCode0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Domain Adversarial Active Learning for Domain Generalization Classification—0
On the Topology Awareness and Generalization Performance of Graph Neural Networks—0
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training—0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
SUPClust: Active Learning at the Boundaries—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Learning of Mealy Machines with Timers—0
Improving Uncertainty Sampling with Bell Curve Weight Function—0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained ModelsCode0
Improve Cost Efficiency of Active Learning over Noisy Dataset—0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching—0
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processingCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Prioritizing Informative Features and Examples for Deep Learning from Noisy DataCode0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems—0
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization—0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Batch Active Learning of Reward Functions from Human Preferences—0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Towards Efficient Active Learning in NLP via Pretrained Representations—0
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data—0
Practice Makes Perfect: Planning to Learn Skill Parameter Policies—0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active LearningCode0
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information—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