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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/tristandeleu/pytorch-maml-rl

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

https://github.com/tueimage/similarity-medical-2017

Exploring the similarity of medical imaging classification problems

https://github.com/turkunlp/megatron-deepspeed

Language Models are Few-Shot Learners

https://github.com/twni2016/pomdp-baselines

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

https://github.com/uber-research/backpropamine

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/uber-research/differentiable-plasticity

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/ucl-dark/pax

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/ucsb-nlp-chang/promptboosting

Making Pre-trained Language Models Better Few-shot Learners

https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction

Matching the Blanks: Distributional Similarity for Relation Learning

https://github.com/ukplab/emnlp2021-prompt-ft-heuristics

Making Pre-trained Language Models Better Few-shot Learners

https://github.com/ulises-jeremias/prototypical-networks-tf

Prototypical Networks for Few-shot Learning

https://github.com/um-arm-lab/efficient-eng-2-ltl

Language Models are Few-Shot Learners

https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/uoe-agents/sami

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/usccolumbia/tsdnn

Meta Pseudo Labels

https://github.com/vamsirk/FewShotLearning

One-Shot Learning for Semantic Segmentation

https://github.com/vamsirk/OneShotSemanticSegmentation

One-Shot Learning for Semantic Segmentation

https://github.com/vanderschaarlab/autoprognosis

AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning

https://github.com/vanderschaarlab/llambo

Large Language Models to Enhance Bayesian Optimization

https://github.com/vanessadamario/understanding_reasoning

CLOSURE: Assessing Systematic Generalization of CLEVR Models

https://github.com/vaseline555/Federated-Learning-in-PyTorch

LEAF: A Benchmark for Federated Settings

https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2

Gotta Learn Fast: A New Benchmark for Generalization in RL

https://github.com/vcskaushik/LLMzip

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ve450su2021-group26/Algorithm

Meta Pseudo Labels

https://github.com/vilm-ai/viet-llm-eval

Language Models are Few-Shot Learners

https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/vincentyu68/policy_transfer

Learning Fast Adaptation with Meta Strategy Optimization

https://github.com/vineetjain96/one-shot-mann

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/vinuni-vishc/few-shot-transformer

A Closer Look at Few-shot Classification

https://github.com/vivekatwal/projects

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/vkverma01/meta-gzsl

A Meta-Learning Framework for Generalized Zero-Shot Learning

https://github.com/volcengine/vegiantmodel

Language Models are Few-Shot Learners

https://github.com/voxelmorph/voxelmorph

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

https://github.com/vsitzmann/light-field-networks

Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering

https://github.com/vsitzmann/metasdf

MetaSDF: Meta-learning Signed Distance Functions

https://github.com/vvrpanda/exppoint-mae

Masked Autoencoders for Point Cloud Self-supervised Learning

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