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 1651–1700 of 3073 papers

TitleStatusHype
ActiveEA: Active Learning for Neural Entity AlignmentCode0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Bayesian Active Summarization—0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up—0
Active learning for interactive satellite image change detection—0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations—0
Synthesizing Video Trajectory Queries—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active Learning for Contextual Search with Binary Feedbacks—0
Automated Seed Quality Testing System using GAN & Active LearningCode0
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis—0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow—0
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Best Practices in Pool-based Active Learning for Image Classification—0
SABAL: Sparse Approximation-based Batch Active Learning—0
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?—0
On Locality in Graph Learning via Graph Neural Network—0
Limitations of Active Learning With Deep Transformer Language Models—0
Sample Complexity of Deep Active Learning—0
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Pretrained models are active learners—0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision—0
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development—0
Active Learning for Argument Mining: A Practical Approach—0
Improving Question Answering Performance Using Knowledge Distillation and Active LearningCode0
Data Summarization via Bilevel Optimization—0
Bayesian Active Learning for Sim-to-Real Robotic Perception—0
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification—0
Active Learning for Argument Strength EstimationCode0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian MomentsCode0
Towards Overcoming Practical Obstacles to Deploying Deep Active Learning—0
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks—0
FOMO: Topics versus documents in legal eDiscovery—0
Mitigating Sampling Bias and Improving Robustness in Active Learning—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Adaptive network reliability analysis: Methodology and applications to power grid—0
Active learning for reducing labeling effort in text classification tasksCode0
Open-World Active Learning with Stacking Ensemble for Self-Driving Cars—0
Show:102550
← PrevPage 34 of 62Next →

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