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 2551–2575 of 3073 papers

TitleStatusHype
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS—0
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning ApproachCode0
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy—0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling—0
Making Efficient Use of a Domain Expert's Time in Relation Extraction—0
Practical Obstacles to Deploying Active Learning—0
Evaluating Active Learning Heuristics for Sequential Diagnosis—0
Towards more Reliable Transfer Learning—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Distilling the Posterior in Bayesian Neural Networks—0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models—0
Cost-Sensitive Active Learning for Dialogue State Tracking—0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Active learning for deep semantic parsing—0
Sampling and Reconstruction of Signals on Product GraphsCode0
Probabilistic Bisection with Spatial Metamodels—0
Cost-effective Object Detection: Active Sample Mining with Switchable Selection CriteriaCode0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Autonomous Wireless Systems with Artificial Intelligence—0
A Practical Incremental Learning Framework For Sparse Entity ExtractionCode0
Dropout-based Active Learning for Regression—0
A Machine-learning framework for automatic reference-free quality assessment in MRI—0
Show:102550
← PrevPage 103 of 123Next →

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