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 1401–1450 of 3073 papers

TitleStatusHype
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
Active Learning of Driving Scenario Trajectories—0
DroidStar: Callback Typestates for Android Classes—0
Active Learning for New Domains in Natural Language Understanding—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Dual Adversarial Network for Deep Active Learning—0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning—0
Analytic Mutual Information in Bayesian Neural Networks—0
DutchSemCor: Targeting the ideal sense-tagged corpus—0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice—0
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling—0
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning—0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms—0
Easy Questions First? A Case Study on Curriculum Learning for Question Answering—0
ED2: Two-stage Active Learning for Error Detection -- Technical Report—0
An Analysis of Active Learning With Uniform Feature Noise—0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course—0
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning—0
Information-Theoretic Active Correlation Clustering—0
Effective Data Selection for Seismic Interpretation through Disagreement—0
Understanding the Eluder Dimension—0
Effective Version Space Reduction for Convolutional Neural Networks—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Efficiency of active learning for the allocation of workers on crowdsourced classification tasks—0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Embodied Active Learning of Relational State Abstractions for Bilevel Planning—0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach—0
Efficient Active Learning of Halfspaces: an Aggressive Approach—0
Efficient active learning of sparse halfspaces—0
Efficient active learning of sparse halfspaces with arbitrary bounded noise—0
Efficient Active Learning with Abstention—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach—0
Efficient Biological Data Acquisition through Inference Set Design—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Efficient Deconvolution in Populational Inverse Problems—0
Active and passive learning of linear separators under log-concave distributions—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