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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://bitbucket.org/antonis/grikoresource

공식 코드

Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource

https://bitbucket.org/arahat/lod-2020

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On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints

https://bitbucket.org/gramacylab/deepgp-ex

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Active Learning for Deep Gaussian Process Surrogates

https://bitbucket.org/smartan117/sgim-yumi-simu

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Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy

https://github.com/000Justin000/ssl_edge

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Graph-based Semi-Supervised & Active Learning for Edge Flows

https://github.com/0liliulei/Mem3D

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Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

https://github.com/1170300714/cbs

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Class Balance Matters to Active Class-Incremental Learning

https://github.com/32af3611/ai4mat-neurips-workshop-2022

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Actively Learning Costly Reward Functions for Reinforcement Learning

https://github.com/AICAN-Research/FAST-Pathology

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Code-free development and deployment of deep segmentation models for digital pathology

https://github.com/AIML-K/ActiveBayesianCausal

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ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments

https://github.com/AP15/NeurIPS_2019

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Correlation Clustering with Adaptive Similarity Queries

https://github.com/AaltoML/BayesVLM

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Post-hoc Probabilistic Vision-Language Models

https://github.com/Aditi138/EntityTargetedActiveLearning

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A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

https://github.com/ArantxaCasanova/ralis

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Reinforced active learning for image segmentation

https://github.com/ArjitJ/DIAL

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Deep Indexed Active Learning for Matching Heterogeneous Entity Representations

https://github.com/ArsamAryandoust/DataSelectionMaps

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Enhanced spatio-temporal electric load forecasts using less data with active deep learning

https://github.com/ArthurHoa/deep-eknn

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Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition

https://github.com/Bidur-Khanal/imbalanced-medical-active-label-cleaning

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Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise

https://github.com/BjoernNieth/LS-Dataset-pruning-in-AT

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Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation

https://github.com/BlackHC/BatchBALD

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BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

https://github.com/BoChen-Daniel/Expert-Linking

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CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

https://github.com/COINtoolbox/RESSPECT

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Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

https://github.com/CoRiis/active-learning-fbgp

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Bayesian Active Learning with Fully Bayesian Gaussian Processes

https://github.com/Coolshanlan/DDFH-active-3Ddet

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Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection

https://github.com/CrickWu/Swtich-DDQ

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Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

https://github.com/DebanjanaKar/ArgFuse

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ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

https://github.com/DeepLearningResearch/DEAL

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DEAL: Deep Evidential Active Learning for Image Classification

https://github.com/ElementAI/active-fairness

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Can Active Learning Preemptively Mitigate Fairness Issues?

https://github.com/ElementAI/baal

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Bayesian active learning for production, a systematic study and a reusable library

https://github.com/ElementAI/synbols

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Synbols: Probing Learning Algorithms with Synthetic Datasets

https://github.com/ElementAI/synbols-benchmarks

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Synbols: Probing Learning Algorithms with Synthetic Datasets

https://github.com/FilVa/PD-Rank

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Ranking with Confidence for Large Scale Comparison Data

https://github.com/GhostAnderson/Nearest-Neighbor-Classifier-with-Margin-Penalty-for-Active-Learning

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Nearest Neighbor Classifier with Margin Penalty for Active Learning

https://github.com/HIT-ICES/AL-NER

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LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition

https://github.com/HaoZhongkai/AS_Molecule

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ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

https://github.com/HelenMa9998/Selective_Uncertainty_AL

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Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation

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