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

Showing 22262250 of 3073 papers

TitleStatusHype
Bucketized Active Sampling for Learning ACOPF0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner0
Active classification with comparison queries0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models0
Active Community Detection with Maximal Expected Model Change0
Active Continual Learning: On Balancing Knowledge Retention and Learnability0
Active covariance estimation by random sub-sampling of variables0
Active Covering0
Active Crowd Counting with Limited Supervision0
Active Curriculum Learning0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures0
Active Deep Decoding of Linear Codes0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings0
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls0
Active Deep Learning for Classification of Hyperspectral Images0
Active Learning Guided by Efficient Surrogate Learners0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys0
Active Deep Learning on Entity Resolution by Risk Sampling0
Active Dialogue Simulation in Conversational Systems0
Active Dictionary Learning in Sparse Representation Based Classification0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes0
Active Discriminative Text Representation Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified