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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/trung6/ActLogReg

공식 코드

Near-Polynomially Competitive Active Logistic Regression

https://github.com/tsa87/TacoGFN-SBDD

공식 코드

TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design

https://github.com/tsunghan-wu/d2ada

공식 코드

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

https://github.com/tsunghan-wu/redal

공식 코드

ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation

https://github.com/tueboesen/a-optimal-active-learning

공식 코드

A-Optimal Active Learning

https://github.com/tzshi/dpp-al-parsing-naacl21

공식 코드

Diversity-Aware Batch Active Learning for Dependency Parsing

https://github.com/ucdvision/low-budget-al

공식 코드

A Simple Baseline for Low-Budget Active Learning

https://github.com/ucsc-real/fairnesswithoutharm

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Fairness Without Harm: An Influence-Guided Active Sampling Approach

https://github.com/udellgroup/oboe

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OBOE: Collaborative Filtering for AutoML Model Selection

https://github.com/uds-lsv/human

공식 코드

HUMAN: Hierarchical Universal Modular ANnotator

https://github.com/uhlerlab/actlearn_optint

공식 코드

Active Learning for Optimal Intervention Design in Causal Models

https://github.com/uhlerlab/causal_mean_matching

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Matching a Desired Causal State via Shift Interventions

https://github.com/ultratykis/guiding-dnns-attention

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Efficient Human-in-the-loop System for Guiding DNNs Attention

https://github.com/unagpal/Active-Learning-in-CNNs-via-Expected-Improvement-Maximization

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Active Learning in CNNs via Expected Improvement Maximization

https://github.com/uq-neusoft-health-data-science/activeea

공식 코드

ActiveEA: Active Learning for Neural Entity Alignment

https://github.com/uqhwen2/FCCM

공식 코드

Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective

https://github.com/uqhwen2/MACAL

공식 코드

Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation

https://github.com/usccolumbia/materialsuq

공식 코드

Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/vafaei-ar/drama

공식 코드

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

https://github.com/vaishwarya96/GroVE

공식 코드

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

https://github.com/valentinmargraf/activelearningpipelines

공식 코드

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

https://github.com/vania-mendonca/Onception

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Onception: Active Learning with Expert Advice for Real World Machine Translation

https://github.com/vardhah/epsilon_weighted_hybrid_query_strategy

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Deep Active Learning for Regression Using $ε$-weighted Hybrid Query Strategy

https://github.com/vgsatorras/few-shot-gnn

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Few-Shot Learning with Graph Neural Networks

https://github.com/vida-nyu/city-surfaces

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CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials

https://github.com/virajprabhu/clue

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Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings

https://github.com/vista-h/hyperpartisan-news-titles

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Computational Assessment of Hyperpartisanship in News Titles

https://github.com/vivian-wzj/dataoptimization-cv

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A Survey of Dataset Refinement for Problems in Computer Vision Datasets

https://github.com/vivian-wzj/datasetrefinement-cv

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A Survey of Dataset Refinement for Problems in Computer Vision Datasets

https://github.com/viviencabannes/active-labeling

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Active Labeling: Streaming Stochastic Gradients

https://github.com/vladan-stojnic/active-outliers

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Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning

https://github.com/vwz/AGE

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Active Learning for Graph Embedding

https://github.com/wagner-group/active-learning

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Continuous Learning for Android Malware Detection

https://github.com/wambodns/gsaal

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Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data

https://github.com/wang-ml-lab/multi-domain-active-learning

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Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees

https://github.com/wangkua1/apd_public

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Adversarial Distillation of Bayesian Neural Network Posteriors

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