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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/asaran/vessal

공식 코드

Streaming Active Learning with Deep Neural Networks

https://github.com/ask-berkeley/stable-training

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Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

https://github.com/asnijders/multi_source_al

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Investigating Multi-source Active Learning for Natural Language Inference

https://github.com/aspuru-guzik-group/janus

공식 코드

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

https://github.com/asreview/paper-asreview-introduction

공식 코드

Open Source Software for Efficient and Transparent Reviews

https://github.com/astrolabsoftware/fink-science

공식 코드

Finding active galactic nuclei through Fink

https://github.com/atomistic-ml/ani-al

공식 코드

Automated discovery of a robust interatomic potential for aluminum

https://github.com/avakanski/creep-uncertainty-quantification

공식 코드

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks

https://github.com/avihu111/typiclust

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Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

https://github.com/baal-org/baal

공식 코드

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning

https://github.com/bardisafa/eoal

공식 코드

Entropic Open-set Active Learning

https://github.com/bargenossar/the-battleship-approach-to-al-of-em-problem

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The Battleship Approach to the Low Resource Entity Matching Problem

https://github.com/barleah/greedyal

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Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval

https://github.com/batistagroup/chemspaceal

공식 코드

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

https://github.com/bdevl/SMO

공식 코드

Self-supervised optimization of random material microstructures in the small-data regime

https://github.com/beatrizmoya/rlfluidperception

공식 코드

A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids

https://github.com/belakaria/al-gsa-dgsms

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Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes

https://github.com/belindal/discrete-active-learning-coref

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Active Learning for Coreference Resolution using Discrete Annotation

https://github.com/bemporad/gnep-learn

공식 코드

An active learning method for solving competitive multi-agent decision-making and control problems

https://github.com/benavoli/SkewGP

공식 코드

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes

https://github.com/bhavyaghai/fluent

공식 코드

Fluent: An AI Augmented Writing Tool for People who Stutter

https://github.com/bidur-khanal/mvaal-medical-images

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M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks

https://github.com/bit-da/duc

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Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

https://github.com/bit-da/eada

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Active Learning for Domain Adaptation: An Energy-Based Approach

https://github.com/bit-da/ripu

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Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation

https://github.com/bjork-crypto/visled-querying

공식 코드

Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

https://github.com/blackhc/2208.00549

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Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities

https://github.com/blackhc/2302.08981

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Black-Box Batch Active Learning for Regression

https://github.com/bo1929/anelfop

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Focusing on Potential Named Entities During Active Label Acquisition

https://github.com/bobxwu/learning-from-rewards-llm-papers

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Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language Models

https://github.com/bonaventuredossou/active_learning_african_asr

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Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models

https://github.com/bonaventuredossou/ece526_course_project

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A Study of Acquisition Functions for Medical Imaging Deep Active Learning

https://github.com/bonaventuredossou/mlm_al

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AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages

https://github.com/boschresearch/amor-struct-gp

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Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels

https://github.com/boschresearch/hierarchical-hyperplane-kernels

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Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems

https://github.com/boschresearch/salmogp

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Safe Active Learning for Multi-Output Gaussian Processes

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