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/Daniil-Selikhanovych/bnn-vi

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

https://github.com/GeorgeLuImmortal/Adaptive-Tuning-Active-Learning

Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets

https://github.com/GeorgeLuImmortal/Effectiveness-of-Pretrained-Transformer-based-Language-Models-in-Active-Learning-for-Labelling-Data

Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets

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

Few-Shot Learning with Graph Neural Networks

https://github.com/IirisSundin/active-learning-for-decision-making

Active Learning for Decision-Making from Imbalanced Observational Data

https://github.com/JohanPhan/Dual_active_sampling_Active_learning

Dual Active Sampling on Batch-Incremental Active Learning

https://github.com/Kaushalya/metal

MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning

https://github.com/KumudaBG/Short-Story-Deep-Active-Learning

A Survey of Deep Active Learning

https://github.com/LONG-9621/IQA_02

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

https://github.com/Lieberk/Paddle-FSL-GNN

Few-Shot Learning with Graph Neural Networks

https://github.com/LukasErlenbach/active_learning_bnn

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

https://github.com/LuxxxLucy/Active-Decision-Set-Induction

An Active Approach for Model Interpretation

https://github.com/Mephisto405/Learning-Loss-for-Active-Learning

Learning Loss for Active Learning

https://github.com/Mind23-2/MindCode-157

Active Learning by Feature Mixing

https://github.com/Mind23-2/MindCode-4/tree/main/gflowcausal

GFlowCausal: Generative Flow Networks for Causal Discovery

https://github.com/MindCode-4/code-12/tree/main/MCA

MCAL: Minimum Cost Human-Machine Active Labeling

https://github.com/Mohammad-Asad-110/Fashion-Recommendation

Buy Me That Look: An Approach for Recommending Similar Fashion Products

https://github.com/Naagar/cornseedsdataset

Automated Seed Quality Testing System using GAN & Active Learning

https://github.com/OREL-group/dBV

Continual Developmental Neurosimulation Using Embodied Computational Agents

https://github.com/Pratik08/vis-dss

Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization

https://github.com/Rapfff/Learning-Probabilistic-models

Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Cost-Effective Active Learning for Deep Image Classification

https://github.com/Riashat/Active-Learning-Bayesian-Convolutional-Neural-Networks

Deep Bayesian Active Learning with Image Data

https://github.com/SeonghwanSeo/RxnFlow

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

https://github.com/ShashSec/SMTI_SA

Towards a relation extraction framework for cyber-security concepts

https://github.com/Shuai-Xie/DEAL

DEAL: Difficulty-aware Active Learning for Semantic Segmentation

https://github.com/SimiPixel/pool_based_active_learning

libact: Pool-based Active Learning in Python

https://github.com/StanfordASL/SCOD

Detecting Underspecification with Local Ensembles

https://github.com/StatNLP/joeynmt

Self-Regulated Interactive Sequence-to-Sequence Learning

https://github.com/Valkyrja3607/VAAL_Segmantation

Variational Adversarial Active Learning

https://github.com/VirgiAgl/DAG-GP

Multi-task Causal Learning with Gaussian Processes

https://github.com/YuuuuXie/Stanene_FLARE

Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene

https://github.com/aai-institute/pyDVL

Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning

https://github.com/aaronchen0316/flare

Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene

https://github.com/acl21/deep-active-learning-pytorch

Towards Robust and Reproducible Active Learning Using Neural Networks

https://github.com/aghasemi/alpud

Active Learning from Positive and Unlabeled Data

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