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 2801–2850 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
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Active Learning for Community Detection in Stochastic Block Models—0
The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents—0
Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction—0
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data—0
Active Learning for Online Recognition of Human Activities from Streaming Videos—0
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification—0
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting—0
Active Learning from Positive and Unlabeled DataCode0
Search Improves Label for Active Learning—0
Submodular Learning and Covering with Response-Dependent Costs—0
Online Active Linear Regression via Thresholding—0
Interactive algorithms: from pool to stream—0
Active Learning Algorithms for Graphical Model Selection—0
Font Identification in Historical Documents Using Active Learning—0
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds—0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models—0
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings—0
The Utility of Abstaining in Binary Classification—0
Refined Error Bounds for Several Learning Algorithms—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