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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/gmc-drl/symbol
공식 코드Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
https://github.com/gmum/few-shot-hypernets-public
공식 코드https://github.com/gmum/hyperplanes
공식 코드https://github.com/gmum/non-gaussian-gaussian-processes
공식 코드https://github.com/gokyeongryeol/MAHA
공식 코드https://github.com/goldblum/AdversarialQuerying
공식 코드Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
https://github.com/goldblum/FeatureClustering
공식 코드Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
https://github.com/gomerudo/nas-env
공식 코드Learning to reinforcement learn for Neural Architecture Search
https://github.com/google-deepmind/neural_networks_solomonoff_induction
공식 코드https://github.com/google-deepmind/thunnini
공식 코드Understanding Prompt Tuning and In-Context Learning via Meta-Learning
https://github.com/google-research-datasets/qameleon
공식 코드https://github.com/google-research/big_transfer
공식 코드https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/cascades
공식 코드https://github.com/google-research/dataset_grouper
공식 코드Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
https://github.com/google-research/federated
공식 코드Federated Reconstruction: Partially Local Federated Learning
https://github.com/google-research/federated/tree/master/adaptive_lr_decay
공식 코드On the Outsized Importance of Learning Rates in Local Update Methods
https://github.com/google-research/federated/tree/master/personalization_benchmark
공식 코드Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
https://github.com/google-research/federated/tree/master/reconstruction
공식 코드Federated Reconstruction: Partially Local Federated Learning
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/STraTA
공식 코드STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
https://github.com/google-research/google-research/tree/master/blur
공식 코드https://github.com/google-research/google-research/tree/master/es_maml
공식 코드https://github.com/google-research/google-research/tree/master/hypertransformer
공식 코드HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
https://github.com/google-research/google-research/tree/master/meta_learning_without_memorization
공식 코드https://github.com/google-research/google-research/tree/master/meta_pseudo_labels
공식 코드https://github.com/google-research/meta-dataset
공식 코드Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
https://github.com/google-research/noisy-fewshot-learning
공식 코드Graph convolutional networks for learning with few clean and many noisy labels
https://github.com/google-research/optformer
공식 코드Towards Learning Universal Hyperparameter Optimizers with Transformers
https://github.com/google-research/prompt-tuning
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/tasseo
공식 코드https://github.com/google-research/self-organising-systems
공식 코드https://github.com/google-research/vmf_embeddings
공식 코드von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
https://github.com/google-research/vmoe
공식 코드https://github.com/google/brain_autorl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.