https://github.com/shunzh/pytorch-maml-rl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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/shyamsn97/hyper-nn
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
https://github.com/sicara/easy-few-shot-learning
https://github.com/sidney1505/arc_maml_transformer
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/silicx/dlsa
https://github.com/simuler/MetaHeac
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
https://github.com/sjduan/LeHDC
MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption
https://github.com/sjn3303123/DL_Reproducibility
https://github.com/sjtu-quant/qlib
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
https://github.com/skingorz/prototypeNet-paddle
https://github.com/skojaku/stacklp
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
https://github.com/skytliang/multi-agents-debate
https://github.com/sl-93/SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
https://github.com/smarton-empower/smarton-ai
https://github.com/smile-data/smile
https://github.com/snap-stanford/graphgym
https://github.com/snorkel-ai/long-context-eval
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
https://github.com/snowkylin/ntm
https://github.com/social-ai-studio/pro-cap
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations
https://github.com/soheeyang/unified-prompt-selection
Calibrate Before Use: Improving Few-Shot Performance of Language Models
https://github.com/sohiniroych/U-net_using_TF2
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images
https://github.com/sohiniroych/Unet-using-TF2
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images
https://github.com/sonnguyentruong129/siamese-oneshot
https://github.com/sorenbouma/keras-oneshot
https://github.com/sourachakra/densesubgraphsaliency
A dense subgraph based algorithm for compact salient image region detection
https://github.com/sourenaKhanzadeh/snakeAi
https://github.com/squeezeailab/squeezellm
https://github.com/srgrace/contextual-compression
Extending Context Window of Large Language Models via Semantic Compression
https://github.com/stanfordbdhg/llama.cpp
https://github.com/stanfordnlp/dsp
https://github.com/stanislavfort/gaussian-prototypical-networks
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
https://github.com/stefanowoerner/medimeta-pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/stepjam/PyRep
Task-Embedded Control Networks for Few-Shot Imitation Learning
https://github.com/stfnmangini/Learning2learn
Learning to learn with quantum neural networks via classical neural networks
https://github.com/sungnyun/cd-fsl
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
https://github.com/sunyilgdx/prompts4keras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.