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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/Zhiwei-Z/PrompLimitTest

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

https://github.com/Zhiwei-Z/SeqPromp

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

https://github.com/Zhiwei-Z/prompzzw

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

https://github.com/ZongrenZou/MHPINN

L-HYDRA: Multi-Head Physics-Informed Neural Networks

https://github.com/a-wozniakowski/scikit-physlearn

Boosting on the shoulders of giants in quantum device calibration

https://github.com/aadityasingh/icl-dynamics

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

https://github.com/aadityasingh/icl-transience

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

https://github.com/aai-institute/pyDVL

Nystrom Method for Accurate and Scalable Implicit Differentiation

https://github.com/abhaskumarsinha/Corpus2GPT

Language Models are Few-Shot Learners

https://github.com/abhaskumarsinha/MinimalGPT

Language Models are Few-Shot Learners

https://github.com/acerbilab/amortized-conditioning-engine

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference

https://github.com/adap/flower

Federated Meta-Learning with Fast Convergence and Efficient Communication

https://github.com/adibiarman/Submodular-Meta-learning

Submodular Meta-Learning

https://github.com/adityagilra/archibrain

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/aethercortex/llama-x

LLaMA: Open and Efficient Foundation Language Models

https://github.com/afoninandrei/opt-ml

Learning to learn by gradient descent by gradient descent

https://github.com/agosiewska/EPP-meta-measure

Interpretable Meta-Measure for Model Performance

https://github.com/ai-s2-lab/fluenteditor

CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

https://github.com/ai21labs/lm-evaluation

Language Models are Few-Shot Learners

https://github.com/ai4co/llm-as-hh

DevFormer: A Symmetric Transformer for Context-Aware Device Placement

https://github.com/ai4co/reevo

DevFormer: A Symmetric Transformer for Context-Aware Device Placement

https://github.com/aimagelab/mammoth

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

https://github.com/aioz-ai/multigraphfl

LEAF: A Benchmark for Federated Settings

https://github.com/akanyaani/miniLLAMA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/akirafukui/vqa-mcb

Compact Bilinear Pooling

https://github.com/akshatgarg99/FewShotLearning

Prototypical Networks for Few-shot Learning

https://github.com/akshaykiranjose/L2O

Learning to learn by gradient descent by gradient descent

https://github.com/akshaymehra24/poisoning_certified_defenses

Penalty Method for Inversion-Free Deep Bilevel Optimization

https://github.com/aldipiroli/semantic_spray_dataset

Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions

https://github.com/alexis-jacq/LOLA

DiCE: The Infinitely Differentiable Monte-Carlo Estimator

https://github.com/alexis-jacq/LOLA_DICE

DiCE: The Infinitely Differentiable Monte-Carlo Estimator

https://github.com/alexub/jax-meta-learning

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

https://github.com/alibaba/EasyTransfer/tree/master/scripts/meta_finetune

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

https://github.com/aliengirlliv/teachable

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

https://github.com/alinlab/metamae

Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder

https://github.com/allen-li1231/refiner-rag

LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

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