https://github.com/meisin/crudeoilnews-corpus
공식 코드CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction
Papers with Code 아카이브
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/melinphd/taal
공식 코드TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
https://github.com/mengf1/PAL
공식 코드Learning how to Active Learn: A Deep Reinforcement Learning Approach
https://github.com/mengf1/trpos
공식 코드Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
https://github.com/mengxiayu/coser
공식 코드Context Selection and Rewriting for Video-based Educational Question Generation
https://github.com/mesophil/myriadal
공식 코드https://github.com/mic-dkfz/attractive_simulations
공식 코드atTRACTive: Semi-automatic white matter tract segmentation using active learning
https://github.com/miccaiif/openal
공식 코드OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
https://github.com/microsoft/TableSense
공식 코드TableSense: Spreadsheet Table Detection with Convolutional Neural Networks
https://github.com/mila-iqia/DeepDrummer
공식 코드DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
https://github.com/millerk22/model-change-paper
공식 코드Model-Change Active Learning in Graph-Based Semi-Supervised Learning
https://github.com/millerk22/rwll_active_learning
공식 코드Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
https://github.com/mingsun-tse/good-da-in-kd
공식 코드What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective
https://github.com/minimel/stochasticbatchal
공식 코드Active learning for medical image segmentation with stochastic batches
https://github.com/minnesotanlp/infoverse
공식 코드infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information
https://github.com/minnesotanlp/select-llm
공식 코드SelectLLM: Can LLMs Select Important Instructions to Annotate?
https://github.com/mir-group/flare
공식 코드https://github.com/mir-group/nequip
공식 코드Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
https://github.com/mirbostani/QA-KD-AL
공식 코드Improving Question Answering Performance Using Knowledge Distillation and Active Learning
https://github.com/mj10/bioseq-gfn-al
공식 코드https://github.com/mkandai/llms-in-the-loop
공식 코드https://github.com/ml-postech/adaptive-superpixel-for-active-learning-in-semantic-segmentation
공식 코드Adaptive Superpixel for Active Learning in Semantic Segmentation
https://github.com/mlpeschl/moral_rl
공식 코드MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning
https://github.com/mlsysops/active-learning-as-a-service
공식 코드Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
https://github.com/mlsysops/alaas
공식 코드Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
https://github.com/mmilkova/nlp-values
공식 코드Detecting value-expressive text posts in Russian social media
https://github.com/modAL-python/modAL
공식 코드https://github.com/mohaimenz/deep_active_featl
공식 코드Deep Active Audio Feature Learning in Resource-Constrained Environments
https://github.com/mourga/contrastive-active-learning
공식 코드On the Importance of Effectively Adapting Pretrained Language Models for Active Learning
https://github.com/mrgiovanni/abdomenatlas
공식 코드AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
https://github.com/mrottmann/DeepBASS
공식 코드https://github.com/multimodal-art-projection/I-SHEEP
공식 코드I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
https://github.com/multimodal-art-projection/criticlean
공식 코드CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization
https://github.com/munanning/mada
공식 코드Multi-Anchor Active Domain Adaptation for Semantic Segmentation
https://github.com/mwalmsley/galaxy-zoo-bayesian-cnn
공식 코드Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
https://github.com/mwerner28/dmgt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.