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 776–800 of 3073 papers

TitleStatusHype
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Are Good Explainers Secretly Human-in-the-Loop Active Learners?—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—0
Active Learning for Vision-Language Models—0
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction—0
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds—0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
A Scalable Algorithm for Active Learning—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks—0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
A Simple Approximation Algorithm for Optimal Decision Tree—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
ALdataset: a benchmark for pool-based active learning—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images—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