Papers with Code 아카이브

Meta-Learning

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하위 태스크: 3
평가표: 4
보존된 평가 기록: 13

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/plkmo/BERT-Relation-Extraction

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

https://github.com/potsawee/maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/prajjwal1/autonomous-object-detection

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

https://github.com/prajjwal1/fluence

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/prolearner/LearningToCompareTF

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/prolearner/hyper-representation

Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning

https://github.com/prolearner/hypergrad/blob/master/examples/iMAML.py

Meta-Learning with Implicit Gradients

https://github.com/prototypical-random-walk/prototypical-random-walk

Semi-Supervised Few-Shot Learning with Prototypical Random Walks

https://github.com/psh150204/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/pujols/Zero-shot-learning-journal

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

https://github.com/pwc-1/Paper-10/tree/main/seggpt

SegGPT: Segmenting Everything In Context

https://github.com/pwc-1/Paper-9/tree/main/6/FSMAFL

Improving Federated Learning Personalization via Model Agnostic Meta Learning

https://github.com/qihongl/dlstm-demo

Been There, Done That: Meta-Learning with Episodic Recall

https://github.com/qitianwu/nodeformer

Matching Networks for One Shot Learning

https://github.com/qwopqwop200/GPTQ-for-LLaMa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/raeidsaqur/mgn

CLOSURE: Assessing Systematic Generalization of CLEVR Models

https://github.com/rafaelsandroni/autolabeling

Want To Reduce Labeling Cost? GPT-3 Can Help

https://github.com/rahul-1996/KGraphs-QA

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/ramanakshay/nanogpt

Language Models are Few-Shot Learners

https://github.com/raminux/python-control/tree/master/published_papers_repo/Automatica

Direct Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness Guarantees

https://github.com/ranpeng-git/few-shot-paddle

Matching Networks for One Shot Learning

https://github.com/ray-project/ray/tree/master/rllib

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/rayat137/VisualPromptGFSS

Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach

https://github.com/rdspring1/lsh_deeplearning

Learning to Remember Rare Events

https://github.com/re-search/DocProduct

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/renmada/ICI-paddle

How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning

https://github.com/replicate/cog_stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/retoschiegg/meta-pseudo-labels

Meta Pseudo Labels

https://github.com/rishikksh20/SiT-pytorch

SiT: Self-supervised vIsion Transformer

https://github.com/rmkemker/EarthMapper

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

https://github.com/roberttwomey/machine-imagination-isea

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-workshop

Language Models are Few-Shot Learners

https://github.com/robotics-4-all/ISSEL-Announcements

Automated Machine Learning Techniques for Data Streams

https://github.com/roop-pal/Meta-Learning-for-StarCraft-II-Minigames

Meta Learning Shared Hierarchies

https://github.com/rpeloff/multimodal_one_shot_learning

Multimodal One-Shot Learning of Speech and Images

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.