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 1051–1100 of 3073 papers

TitleStatusHype
Frugal Reinforcement-based Active Learning—0
Evaluating Zero-cost Active Learning for Object Detection—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object DetectionCode1
Dissimilar Nodes Improve Graph Active LearningCode0
Active learning using adaptable task-based prioritisation—0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
Margin-based sampling in high dimensions: When being active is less efficient than staying passive—0
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification—0
ALARM: Active LeArning of Rowhammer Mitigations—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Deep Active Learning for Computer Vision: Past and Future—0
Combining Data Generation and Active Learning for Low-Resource Question AnsweringCode0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
Responsible Active Learning via Human-in-the-loop Peer Study—0
PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online DataCode1
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning—0
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings—0
Plug and Play Active Learning for Object DetectionCode1
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Finding active galactic nuclei through FinkCode1
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors—0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Active Learning by Query by Committee with Robust Divergences—0
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials—0
Active Learning with Expected Error Reduction—0
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regressionCode1
Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?—0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
An Efficient Active Learning Pipeline for Legal Text Classification—0
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic SegmentationCode1
Understanding Approximation for Bayesian Inference in Neural Networks—0
ALANNO: An Active Learning Annotation System for Mortals—0
Active Learning with Tabular Language Models—0
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler—0
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Fine-Tuning Language Models via Epistemic Neural NetworksCode1
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop—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