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Active Learning

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보존된 평가 기록: 7

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/cvjena/ITAL

공식 코드

Information-Theoretic Active Learning for Content-Based Image Retrieval

https://github.com/cylindrical-penguin/Curiosity-Driven-RL-for-PFM

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Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy

https://github.com/daintlab/confidence-aware-learning

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Confidence-Aware Learning for Deep Neural Networks

https://github.com/dakot/probal

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Toward Optimal Probabilistic Active Learning Using a Bayesian Approach

https://github.com/dancewithDianTong/COMBOO

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Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation

https://github.com/danielsun94/conversational_diagnosis

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Conversational Disease Diagnosis via External Planner-Controlled Large Language Models

https://github.com/danielt17/Deep-Neural-Trees-Simulation-Environment

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Stealing Black-Box Functionality Using The Deep Neural Tree Architecture

https://github.com/danikiyasseh/SoCal

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SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

https://github.com/danikiyasseh/SoQal

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SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

https://github.com/data61/aboleth

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Noise Contrastive Priors for Functional Uncertainty

https://github.com/dataiku-research/openal

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OpenAL: Evaluation and Interpretation of Active Learning Strategies

https://github.com/dataiku-research/paper_ial_2021

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Sample Noise Impact on Active Learning

https://github.com/dataiku-research/sample_noise_impact_on_active_learning

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Sample Noise Impact on Active Learning

https://github.com/davidjurgens/potato

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POTATO: The Portable Text Annotation Tool

https://github.com/davidtw999/bemps

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Diversity Enhanced Active Learning with Strictly Proper Scoring Rules

https://github.com/davidzhangyuanhan/bamboo

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Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

https://github.com/decile-team/distil

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SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios

https://github.com/deepmicroscopy/adaptiveregionselection

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Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation

https://github.com/deepmind/neural_testbed

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Fine-Tuning Language Models via Epistemic Neural Networks

https://github.com/dholzmueller/bmdal_reg

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A Framework and Benchmark for Deep Batch Active Learning for Regression

https://github.com/dhuseljic/dal-toolbox

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Efficient Bayesian Updates for Deep Learning via Laplace Approximations

https://github.com/diku-dk/credanno

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cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis

https://github.com/divelab/atta

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Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

https://github.com/divyakraman/salad_sourcefreeactivelabelagnosticdomainadaptation

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SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection

https://github.com/dlgroupuoft/calpric

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Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning

https://github.com/dmadras/local-ensembles

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Detecting Underspecification with Local Ensembles

https://github.com/dmar-bonn/ipp-al-framework

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An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping

https://github.com/dmar-bonn/ipp-ssl

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Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning

https://github.com/dmusekamp/al4pde

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Active Learning for Neural PDE Solvers

https://github.com/dongchen06/label-efficient-in-agriculture

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Label-Efficient Learning in Agriculture: A Comprehensive Review

https://github.com/dongwoolee-eli/nslpfn

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Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks

https://github.com/dragonlzm/ISAL

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Influence Selection for Active Learning

https://github.com/dralgroup/mlatom

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Physics-informed active learning for accelerating quantum chemical simulations

https://github.com/drewprinster/conformal-mfcs

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Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)

https://github.com/drimpossible/Sampling-Bias-Active-Learning

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Sampling Bias in Deep Active Classification: An Empirical Study

https://github.com/drjeong/digitperceptron

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Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation

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