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Active Learning

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하위 태스크: 1
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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/olivesgatech/gauss

공식 코드

Gaussian Switch Sampling: A Second Order Approach to Active Learning

https://github.com/olivesgatech/introspective-learning

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Introspective Learning : A Two-Stage Approach for Inference in Neural Networks

https://github.com/olivesgatech/man-recon

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Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation

https://github.com/omegafragger/DDU

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Deep Deterministic Uncertainty: A Simple Baseline

https://github.com/openai/moderation-api-release

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A Holistic Approach to Undesired Content Detection in the Real World

https://github.com/openai/prm800k

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Let's Verify Step by Step

https://github.com/openmatch/activerag

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ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents

https://github.com/ortonwang/pcdal

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PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

https://github.com/osimeoni/RethinkingDeepActiveLearning

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Rethinking deep active learning: Using unlabeled data at model training

https://github.com/ozansener/active_learning_coreset

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Active Learning for Convolutional Neural Networks: A Core-Set Approach

https://github.com/parmoo/cfr-materials

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A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

https://github.com/pathak22/exploration-by-disagreement

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Self-Supervised Exploration via Disagreement

https://github.com/patrickzh/deepcore

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DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

https://github.com/peiyunh/alpf

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Active Learning with Partial Feedback

https://github.com/pgiouroukis/dual

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DUAL: Diversity and Uncertainty Active Learning for Text Summarization

https://github.com/philip-schrodt/ngec

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Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks

https://github.com/philipp-kohl/active-learning-evaluation-framework

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ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP

https://github.com/philipp-kohl/scoping-review-active-learning-er

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Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition Tasks

https://github.com/pieter-jacobs/bachelor-thesis

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Active learning for reducing labeling effort in text classification tasks

https://github.com/pieterblok/boxal

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Improved detection of discarded fish species through BoxAL active learning

https://github.com/pieterblok/maskal

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Active learning with MaskAL reduces annotation effort for training Mask R-CNN

https://github.com/pietrolesci/anchoral

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AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets

https://github.com/pjlintw/sample-size

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Revisiting Sample Size Determination in Natural Language Understanding

https://github.com/plai-group/simple-cnaps

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Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

https://github.com/pmalekzadeh/MM-KTD

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MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning

https://github.com/posydon-code/posydon

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Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations

https://github.com/pranav-s/polymersolarcellsml

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Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing

https://github.com/prateekmunjal/torchal

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Towards Robust and Reproducible Active Learning Using Neural Networks

https://github.com/previtus/ChangeDetectionProject

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Deep Active Learning in Remote Sensing for data efficient Change Detection

https://github.com/project-monai/monailabel

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MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

https://github.com/pythonpanda2/active-learning-md

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Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide

https://github.com/qd-seg/quickdraw

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QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation

https://github.com/qphong/bes-mp

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An Information-Theoretic Framework for Unifying Active Learning Problems

https://github.com/rainavyas/img_attackability

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Identifying Adversarially Attackable and Robust Samples

https://github.com/raymin0223/logo

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Re-thinking Federated Active Learning based on Inter-class Diversity

https://github.com/razvancaramalau/Sequential-GCN-for-Active-Learning

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Sequential Graph Convolutional Network for Active Learning

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