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 2801–2825 of 3073 papers

TitleStatusHype
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media—0
Easy Questions First? A Case Study on Curriculum Learning for Question Answering—0
Unsupervised Document Classification with Informed Topic Models—0
Active Learning for Dependency Parsing with Partial Annotation—0
ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling—0
Inferring solutions of differential equations using noisy multi-fidelity dataCode0
Stream-based Online Active Learning in a Contextual Multi-Armed Bandit Framework—0
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Optimiser l'adaptation en ligne d'un module de compr\'ehension de la parole avec un algorithme de bandit contre un adversaire (Adversarial bandit for optimising online active learning of spoken language understanding)—0
Geometry in Active Learning for Binary and Multi-class Image Segmentation—0
Active Discriminative Text Representation Learning—0
Cooperative Inverse Reinforcement LearningCode0
Addressing Limited Data for Textual Entailment Across Domains—0
Adaptive Submodular Ranking and Routing—0
The Solution Path Algorithm for Identity-Aware Multi-Object Tracking—0
Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation—0
Multilinear Hyperplane Hashing—0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification—0
Investigating Active Learning for Short-Answer Scoring—0
Towards ontology driven learning of visual concept detectors—0
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning—0
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems—0
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests—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