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 601–650 of 3073 papers

TitleStatusHype
A Comparison of Strategies for Source-Free Domain Adaptation—0
Analytic Mutual Information in Bayesian Neural Networks—0
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning—0
Active Learning in Symbolic Regression with Physical Constraints—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Active Learning by Query by Committee with Robust Divergences—0
Active Learning in Recommendation Systems with Multi-level User Preferences—0
Active Learning in Physics: From 101, to Progress, and Perspective—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
Active Learning in Noisy Conditions for Spoken Language Understanding—0
ACIL: Active Class Incremental Learning for Image Classification—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Active Community Detection with Maximal Expected Model Change—0
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Active Learning of Driving Scenario Trajectories—0
Active Learning in Gaussian Process State Space Model—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Fast Rates in Pool-Based Batch Active Learning—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
Active-learning-based non-intrusive Model Order Reduction—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
Active Learning from Weak and Strong Labelers—0
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Active Learning-based Model Predictive Coverage Control—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Active Learning from Peers—0
Active classification with comparison queries—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
An Active Parameter Learning Approach to The Identification of Safe Regions—0
Active Learning from Imperfect Labelers—0
Active Learning from Crowd in Document Screening—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—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