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 2626–2650 of 3073 papers

TitleStatusHype
Deep Bayesian Active Semi-Supervised LearningCode0
Active model learning and diverse action sampling for task and motion planningCode0
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling—0
Adversarial Active Learning for Deep Networks: a Margin Based Approach—0
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active LearningCode0
Best Arm Identification for Contaminated Bandits—0
Active Learning with Logged Data—0
Active Learning with Partial FeedbackCode0
Distributional Term Set Expansion—0
Fast Interactive Image Retrieval using large-scale unlabeled data—0
Thompson Sampling for Dynamic Pricing—0
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings—0
Greedy Active Learning Algorithm for Logistic Regression Models—0
Improving Active Learning in Systematic Reviews—0
Less is more: sampling chemical space with active learningCode0
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection—0
Impact of Batch Size on Stopping Active Learning for Text Classification—0
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Efficient Test Collection Construction via Active LearningCode0
Localization-Aware Active Learning for Object Detection—0
Active Community Detection with Maximal Expected Model Change—0
Semi-automated Annotation of Signal Events in Clinical EEG Data—0
Few-Shot Learning with Graph Neural NetworksCode0
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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