Papers with Code 아카이브
Meta-Learning
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Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mgkumar138/Oneshot_Reservoir
공식 코드One-shot learning of paired association navigation with biologically plausible schemas
https://github.com/mgkumar138/schema4one
공식 코드One-shot learning of paired association navigation with biologically plausible schemas
https://github.com/mgradyn/AniWho
공식 코드AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images
https://github.com/mi2datalab/sefnet-healthcare
공식 코드SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
https://github.com/mibastro/VPE
공식 코드Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
https://github.com/miccaiif/top
공식 코드https://github.com/michaelkonobeev/meta
공식 코드Trace norm regularization for multi-task learning with scarce data
https://github.com/michalislazarou/a2lp
공식 코드Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
https://github.com/michalislazarou/ilpc
공식 코드Iterative label cleaning for transductive and semi-supervised few-shot learning
https://github.com/michalislazarou/tfh_fewshot
공식 코드https://github.com/microsoft/DeepSpeed
공식 코드https://github.com/microsoft/LLMLingua
공식 코드LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
https://github.com/microsoft/MetaXL
공식 코드MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
https://github.com/microsoft/ORBIT-Dataset
공식 코드ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
https://github.com/microsoft/banknote-net
공식 코드BankNote-Net: Open dataset for assistive universal currency recognition
https://github.com/microsoft/clues
공식 코드CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
https://github.com/microsoft/list
공식 코드LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners
https://github.com/microsoft/mlc
공식 코드https://github.com/microsoft/ticl
공식 코드MotherNet: Fast Training and Inference via Hyper-Network Transformers
https://github.com/microsoft/unilm
공식 코드https://github.com/microsoft/vert-papers
공식 코드Decomposed Meta-Learning for Few-Shot Named Entity Recognition
https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cross
공식 코드Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
https://github.com/mifei/st-tod
공식 코드Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems
https://github.com/migs2021/migs
공식 코드https://github.com/mihir3009/in-boxbart
공식 코드In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
https://github.com/mikehuisman/lstm-fewshotlearning-oplstm
공식 코드https://github.com/mikehuisman/revisiting-learned-optimizers
공식 코드https://github.com/mikehuisman/subspace-adaptation-prior
공식 코드https://github.com/mikehuisman/transfer-meta-feature-representations
공식 코드Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques
https://github.com/mikewangwzhl/multitask-finetuning_clip
공식 코드Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning
https://github.com/milanlproc/interpretability-mt-gender-bias
공식 코드https://github.com/mileyan/simple_shot
공식 코드SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
https://github.com/milliemaoo/prompt-position
공식 코드https://github.com/miltonllera/meta-evolved-dev
공식 코드https://github.com/mimbres/SeqSkip
공식 코드Sequential Skip Prediction with Few-shot in Streamed Music Contents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.