Papers with Code 아카이브

Active Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 1
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/ssydasheng/predictive-correlation-benchmark

공식 코드

Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?

https://github.com/stacs-cp/CP2024-Frugal

공식 코드

Frugal Algorithm Selection

https://github.com/stanford-futuredata/selection-via-proxy

공식 코드

Selection via Proxy: Efficient Data Selection for Deep Learning

https://github.com/stanfordasl/data-lifecycle-management

공식 코드

Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications

https://github.com/stat-ml/large_batch_bald

공식 코드

Scalable Batch Acquisition for Deep Bayesian Active Learning

https://github.com/stratosphereips/model_extraction_malware

공식 코드

Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions

https://github.com/sumanyumuku98/contextual-bias

공식 코드

Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias

https://github.com/sumitsk/algp

공식 코드

Active Learning with Gaussian Processes for High Throughput Phenotyping

https://github.com/sungbinlim/GBS

공식 코드

Neural Bootstrapper

https://github.com/sungbinlim/neuboots

공식 코드

Neural Bootstrapper

https://github.com/superuier/mdal-pipeline

공식 코드

Multi-Domain Active Learning: Literature Review and Comparative Study

https://github.com/surajkothawade/talisman

공식 코드

TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information

https://github.com/svenl13/gale

공식 코드

Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit

https://github.com/symanto-research/active-few-shot-learning

공식 코드

Active Few-Shot Learning with FASL

https://github.com/synsin0/effocc

공식 코드

EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network

https://github.com/systemt2021/slicetuner

공식 코드

Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models

https://github.com/tansey-lab/pdbal

공식 코드

Targeted active learning for probabilistic models

https://github.com/tencentyouturesearch/activelearning-sdm

공식 코드

Learning Distinctive Margin toward Active Domain Adaptation

https://github.com/testingautomated-usi/corrupted-text

공식 코드

Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)

https://github.com/testingautomated-usi/dnn-tip

공식 코드

Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)

https://github.com/testingautomated-usi/simple-tip

공식 코드

Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)

https://github.com/thatswhatsimonsaid/RashomonActiveLearning

공식 코드

Unique Rashomon Sets for Robust Active Learning

https://github.com/thetna/endo-active-learn

공식 코드

Task-Aware Active Learning for Endoscopic Image Analysis

https://github.com/thomastkok/active-selection-of-classification-features

공식 코드

Active Selection of Classification Features

https://github.com/thufdd/jiaolongdsms_datasets

공식 코드

Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

https://github.com/thufdd/thu-concept-drift-datasets

공식 코드

Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

https://github.com/tijana-zrnic/active-inference

공식 코드

Active Statistical Inference

https://github.com/timsey/npal

공식 코드

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes

https://github.com/tiosisai/bioac-active-learning-eusipco-2025

공식 코드

Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection

https://github.com/tjunlp-lab/aprt

공식 코드

Automated Progressive Red Teaming

https://github.com/tkanchin/adaptive_reg_active_learning

공식 코드

A Simple yet Brisk and Efficient Active Learning Platform for Text Classification

https://github.com/tom-andersson/eds2022-convgnp-sensor-placement

공식 코드

Environmental Sensor Placement with Convolutional Gaussian Neural Processes

https://github.com/tongshuangwu/scattershot

공식 코드

ScatterShot: Interactive In-context Example Curation for Text Transformation

https://github.com/topoxlab/consistency-ranking-loss

공식 코드

Confidence Estimation Using Unlabeled Data

https://github.com/tpopordanoska/calibration_and_bias

공식 코드

On the relationship between calibrated predictors and unbiased volume estimation

https://github.com/trent-b/unisel

공식 코드

Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection

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