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 1251–1275 of 3073 papers

TitleStatusHype
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls—0
A quantum active learning algorithm for sampling against adversarial attacks—0
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation—0
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks—0
Active Learning of Sequential Transducers with Side Information about the Domain—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
A Pre-trained Data Deduplication Model based on Active Learning—0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
Active Learning of Piecewise Gaussian Process Surrogates—0
Active Learning for Automated Visual Inspection of Manufactured Products—0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
A Bayesian Active Learning Approach to Comparative Judgement—0
Bayesian Active Learning for Semantic Segmentation—0
Active Learning for Binary Classification with Abstention—0
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Applied metamodelling for ATM performance simulations—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
Active learning of neural response functions with Gaussian processes—0
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
Active learning of neural population dynamics using two-photon holographic optogenetics—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