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 2551–2600 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
On the Relationship between Data Efficiency and Error for Uncertainty SamplingCode0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning—0
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian ResourceCode0
Bayesian Model-Agnostic Meta-LearningCode1
Probabilistic Model-Agnostic Meta-Learning—0
Model-based active learning to detect isometric deformable objects in the wild with deep architectures—0
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data—0
Finding Convincing Arguments Using Scalable Bayesian Preference LearningCode0
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus—0
The Power of Ensembles for Active Learning in Image Classification—0
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]Code0
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning—0
Active and Adaptive Sequential learning—0
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-OrganizationCode0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning—0
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation—0
Deep Active Learning for Anomaly Detection—0
Distribution Aware Active Learning—0
Teacher's Perception in the Classroom—0
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification—0
Single Shot Active Learning using Pseudo AnnotatorsCode0
Progress & Compress: A scalable framework for continual learningCode0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—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