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Active Learning

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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/agusdmb/Active-Learning-Framework

Active Learning Using Uncertainty Information

https://github.com/airi-institute/al_toolbox

Bayesian Active Learning for Classification and Preference Learning

https://github.com/alemol/flat

Active learning in annotating micro-blogs dealing with e-reputation

https://github.com/alexandervnikitin/tsgm

Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

https://github.com/alexhernandezgarcia/gflownet

Multi-Fidelity Active Learning with GFlowNets

https://github.com/alonzi/cv

The Future of Data Science Education

https://github.com/andreasgrv/emojivote

Deep Deterministic Uncertainty: A Simple Baseline

https://github.com/andris955/diplomaterv

Learning to Multi-Task by Active Sampling

https://github.com/anitoanto/alus-package

covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learning

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

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

https://github.com/anoopsanka/retinal_oct

SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology

https://github.com/archit120/Risk-Aware-Active-IRL

Risk-Aware Active Inverse Reinforcement Learning

https://github.com/argonne-lcf/active-learning-md

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

https://github.com/ariapoy/alipy

ALiPy: Active Learning in Python

https://github.com/arthurbra/stm-corpus/blob/master/README.md

Domain-independent Extraction of Scientific Concepts from Research Articles

https://github.com/ashley-kim521/11785-project-active-learning-for-YOLO

Learning Loss for Active Learning

https://github.com/astirn/variational-variance

Reliable training and estimation of variance networks

https://github.com/axondeepseg/axondeepseg

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/benjaminbeilharz/hierarchical-reinforcement-learning

Self-Regulated Interactive Sequence-to-Sequence Learning

https://github.com/blackhc/active-bayesian-coresets

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

https://github.com/blackhc/active_learning_redux

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

https://github.com/brain-research/ncp

Noise Contrastive Priors for Functional Uncertainty

https://github.com/broshanfekr/Active-Semi-Supervised-Learning-Using-Sampling-Theory-for-Graph-signals

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection

Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision

https://github.com/cambridge-mlg/BALaudiogram

Bayesian Active Learning for Classification and Preference Learning

https://github.com/chanlaw/assistive-bandits

Cooperative Inverse Reinforcement Learning

https://github.com/cleanlab/cleanlab

ActiveLab: Active Learning with Re-Labeling by Multiple Annotators

https://github.com/cnguyen10/few_shot_meta_learning

Probabilistic Model-Agnostic Meta-Learning

https://github.com/code-implementation1/Code9/tree/main/cflownets

CFlowNets: Continuous Control with Generative Flow Networks

https://github.com/code-implementation1/Code9/tree/main/gflowcausal

GFlowCausal: Generative Flow Networks for Causal Discovery

https://github.com/consequentai/fneval

Let's Verify Step by Step

https://github.com/cxjdavin/active-causal-structure-learning-with-advice

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

https://github.com/damienlancry/DBAL

Deep Bayesian Active Learning with Image Data

https://github.com/debabrota-basu/marich

DeepAL: Deep Active Learning in Python

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

Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision

https://github.com/deep-spin/sigmorphon-seq2seq

Self-Regulated Interactive Sequence-to-Sequence Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.