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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/fsahli/MFclass

공식 코드

Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models

https://github.com/fuqichen1998/SequentialVotingDet

공식 코드

Sequential Voting with Relational Box Fields for Active Object Detection

https://github.com/furkancanturk/explainable_active_learning

공식 코드

Explainable Active Learning for Preference Elicitation

https://github.com/gabrieleciravegna/knowledge-driven-active-learning

공식 코드

Knowledge-driven Active Learning

https://github.com/gaurav71531/DeAn

공식 코드

Noisy Batch Active Learning with Deterministic Annealing

https://github.com/geifmany/Active-inas

공식 코드

Deep Active Learning with a Neural Architecture Search

https://github.com/genedisco/genedisco

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GeneDisco: A Benchmark for Experimental Design in Drug Discovery

https://github.com/georgedeath/egreedy

공식 코드

Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

https://github.com/gfnorg/multi-objective-gfn

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Multi-Objective GFlowNets

https://github.com/giparisi/GDM

공식 코드

Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

https://github.com/gitmehrdad/face

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Face: Fast, Accurate and Context-Aware Audio Annotation and Classification

https://github.com/gknb/alband

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An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

https://github.com/gmcampbell/spa_design

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Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators

https://github.com/gokyeongryeol/tbu

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Transferable Candidate Proposal with Bounded Uncertainty

https://github.com/google-research/arxiv-latex-cleaner

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Computational Job Market Analysis with Natural Language Processing

https://github.com/google-research/google-research

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Tackling Provably Hard Representative Selection via Graph Neural Networks

https://github.com/google-research/mma

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Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels

https://github.com/google/goldfinch

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The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

https://github.com/google/uncertainty-baselines

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Plex: Towards Reliability using Pretrained Large Model Extensions

https://github.com/grant-m-s/astronomical

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AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning

https://github.com/gudovskiy/al-fk-self-supervision

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Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision

https://github.com/haizailache999/autoal

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AutoAL: Automated Active Learning with Differentiable Query Strategy Search

https://github.com/halolimat/SpExtor

공식 코드

A Practical Incremental Learning Framework For Sparse Entity Extraction

https://github.com/han-yuan-med/foundation-model

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Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning

https://github.com/hangqiu/mcal

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MCAL: Minimum Cost Human-Machine Active Labeling

https://github.com/hankuipeng/WSSR

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Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning

https://github.com/hannahkirk/activetransformers-for-abusivelanguage

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Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning

https://github.com/haonanpeng/active_syn_generator

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Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation

https://github.com/healthml/active-segmentation

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Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation

https://github.com/healthy-ai/guro

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Active Preference Learning for Ordering Items In- and Out-of-sample

https://github.com/hideaki-ishibashi/stopping_AL

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Stopping Criterion for Active Learning Based on Error Stability

https://github.com/hitachinsk/tha

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A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty

https://github.com/hltcoe/hc4

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HC4: A New Suite of Test Collections for Ad Hoc CLIR

https://github.com/hoolagans/stackgp

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Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression

https://github.com/hrouzegar/enhancing-text-classification-through-llm-driven-active-learning-and-human-annotation

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Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation

https://github.com/huangdaolang/aline

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ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition

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