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 1901–1950 of 3073 papers

TitleStatusHype
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Sequential Adaptive Design for Jump Regression Estimation—0
Sequential Design for Optimal Stopping Problems—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF—0
SHEF-Lite: When Less is More for Translation Quality Estimation—0
SHINRA: Structuring Wikipedia by Collaborative Contribution—0
Ship Detection in SAR Images with Human-in-the-Loop—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data—0
Similarity Search for Efficient Active Learning and Search of Rare Concepts—0
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning—0
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization—0
Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback—0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains—0
Single Image Object Counting and Localizing using Active-Learning—0
Single-Modal Entropy based Active Learning for Visual Question Answering—0
Small-GAN: Speeding Up GAN Training Using Core-sets—0
Small-Text: Active Learning for Text Classification in Python—0
Smart Active Sampling to enhance Quality Assurance Efficiency—0
SMART: An Open Source Data Labeling Platform for Supervised Learning—0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
Smooth Pseudo-Labeling—0
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis—0
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals—0
Social Media Predictive Analytics—0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
Solving Multi-Arm Bandit Using a Few Bits of Communication—0
Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction—0
Sophisticated Inference—0
SoQal: Selective Oracle Questioning in Active Learning—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Spatially regularized active diffusion learning for high-dimensional images—0
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning—0
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry—0
Sprucing up the trees -- Error detection in treebanks—0
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions—0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning—0
STARdom: an architecture for trusted and secure human-centered manufacturing systems—0
Statistical Active Learning Algorithms—0
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy—0
Statistical Hardware Design With Multi-model Active Learning—0
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains—0
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning—0
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Stochastic Descent Analysis of Representation Learning Algorithms—0
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic—0
Stochastic Submodular Maximization via Polynomial Estimators—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