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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/mwien/counting-with-applications

공식 코드

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

https://github.com/mxschmdt/mrqa-gen-al

공식 코드

Combining Data Generation and Active Learning for Low-Resource Question Answering

https://github.com/nab170130/new_streamline

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STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings

https://github.com/nab170130/stencil

공식 코드

STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning

https://github.com/namkoong-lab/inductive-biases-exchangeable-sequence

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Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling

https://github.com/nec-research/alebrew

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Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials

https://github.com/neptunebio/naiad

공식 코드

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space

https://github.com/nercms-mmap/caaf

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Confidence-Aware Active Feedback for Interactive Instance Search

https://github.com/nerminsamet/seedal

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You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation

https://github.com/netflix/videoannotator

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Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learning

https://github.com/neuhai/explanation-enriched-active-learning

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Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture

https://github.com/neuralchecker/pyModelExtractor

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Towards Efficient Active Learning of PDFA

https://github.com/neuralchecker/pymodelextractor_towards_efficient_active_learning_of_PDFA_experiments

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Towards Efficient Active Learning of PDFA

https://github.com/neuropoly/deep-active-learning

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Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/newslotdetection/newslotdetection

공식 코드

Actively Discovering New Slots for Task-oriented Conversation

https://github.com/newspanda-wwf-cmu/weekly-pipeline

공식 코드

NewsPanda: Media Monitoring for Timely Conservation Action

https://github.com/niansong1996/wassp

공식 코드

Merging Weak and Active Supervision for Semantic Parsing

https://github.com/nihalsid/ViewAL

공식 코드

ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation

https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts

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Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure

https://github.com/nikita-0209/mf-al-gfn

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Multi-Fidelity Active Learning with GFlowNets

https://github.com/ningkp/LfOSA

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Active Learning for Open-set Annotation

https://github.com/nirjhar-das/active-preference-optimization

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Active Preference Optimization for Sample Efficient RLHF

https://github.com/nju-websoft/daakg

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Deep Active Alignment of Knowledge Graph Entities and Schemata

https://github.com/nkees/active-learning-argument-strength

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Active Learning for Argument Strength Estimation

https://github.com/nlp-uoregon/famie

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FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

https://github.com/noabdavid/activeBNSL

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Active Structure Learning of Bayesian Networks in an Observational Setting

https://github.com/noyomer/rfan-trial

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Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions

https://github.com/nphdang/ccral

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Efficient Classification with Counterfactual Reasoning and Active Learning

https://github.com/ntucllab/libact

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libact: Pool-based Active Learning in Python

https://github.com/nus-hpc-ai-lab/info-coevolution

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Info-Coevolution: An Efficient Framework for Data Model Coevolution

https://github.com/nvlabs/al-mdn

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Active Learning for Deep Object Detection via Probabilistic Modeling

https://github.com/nwaftp23/nflows_epistemic

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Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling

https://github.com/oatml/causal-bald

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Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

https://github.com/olivesgatech/RoSE

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Targeting Negative Flips in Active Learning using Validation Sets

https://github.com/olivesgatech/clinical-trial-active-learning

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Clinical Trial Active Learning

https://github.com/olivesgatech/focal_dataset

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FOCAL: A Cost-Aware Video Dataset for Active Learning

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