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 1251–1300 of 3073 papers

TitleStatusHype
THMA: Tencent HD Map AI System for Creating HD Map Annotations—0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models—0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
Predicting article quality scores with machine learning: The UK Research Excellence Framework—0
MAViC: Multimodal Active Learning for Video Captioning—0
Frugal Reinforcement-based Active Learning—0
Evaluating Zero-cost Active Learning for Object Detection—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
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
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—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
Combining Data Generation and Active Learning for Low-Resource Question AnsweringCode0
Deep Active Learning for Computer Vision: Past and Future—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Responsible Active Learning via Human-in-the-loop Peer Study—0
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning—0
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
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
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
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
An Efficient Active Learning Pipeline for Legal Text Classification—0
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
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Neural Active Learning on Heteroskedastic DistributionsCode0
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop—0
Batch Active Learning from the Perspective of Sparse Approximation—0
Oracle-guided Contrastive Clustering—0
Entity Matching by Pool-based Active Learning—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