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/hustyyq/where-to-edit

공식 코드

Learning Where to Edit Vision Transformers

https://github.com/hwxu20/gps

공식 코드

GPS: Genetic Prompt Search for Efficient Few-shot Learning

https://github.com/hyheng/std-plm

공식 코드

STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM

https://github.com/hyperconnect/fasten

공식 코드

Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

https://github.com/hytseng0509/CrossDomainFewShot

공식 코드

Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation

https://github.com/hytseng0509/DropGrad

공식 코드

Regularizing Meta-Learning via Gradient Dropout

https://github.com/hzakerinia/flexible-pac-bayes-meta-learning

공식 코드

More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms

https://github.com/hzhupku/DCNet

공식 코드

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

https://github.com/iCVTEAM/CBMNet

공식 코드

Cooperative Bi-path Metric for Few-shot Learning

https://github.com/iQua/CVPR2020-Shoestring

공식 코드

Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data

https://github.com/ibm-granite/granite-tsfm

공식 코드

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

https://github.com/ibm/fastfit

공식 코드

When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes

https://github.com/ibm/kgi-slot-filling

공식 코드

Robust Retrieval Augmented Generation for Zero-shot Slot Filling

https://github.com/ibrahimself/fewtopner

공식 코드

FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework

https://github.com/icantnamemyself/gpht

공식 코드

Generative Pretrained Hierarchical Transformer for Time Series Forecasting

https://github.com/icey-zhang/diffclip

공식 코드

DiffCLIP: Few-shot Language-driven Multimodal Classifier

https://github.com/icgy96/dnpg

공식 코드

Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition

https://github.com/icgy96/micm

공식 코드

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning

https://github.com/iclr2022anon/fast_finite_width_ntk

공식 코드

Fast Finite Width Neural Tangent Kernel

https://github.com/iclr23submission6251/adbo

공식 코드

Asynchronous Distributed Bilevel Optimization

https://github.com/icvteam/dara

공식 코드

Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning

https://github.com/idea-iitd/gshot

공식 코드

GSHOT: Few-shot Generative Modeling of Labeled Graphs

https://github.com/ideas-labo/sempl

공식 코드

Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning

https://github.com/idiap/abroad-re

공식 코드

Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach

https://github.com/idiap/gme-sampler

공식 코드

Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach

https://github.com/idrori/mathq

공식 코드

A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level

https://github.com/idsia/automated-cl

공식 코드

Automating Continual Learning

https://github.com/idsia/modern-srwm

공식 코드

A Modern Self-Referential Weight Matrix That Learns to Modify Itself

https://github.com/iedwardwangi/MetaAdapter

공식 코드

On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment

https://github.com/iesl/leopard

공식 코드

Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks

https://github.com/iesl/metanlp

공식 코드

Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks

https://github.com/igitugraz/memorydependentcomputation

공식 코드

Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity

https://github.com/ihsaan-ullah/meta-album

공식 코드

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

https://github.com/ihsanullah2131/meta-album

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Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

https://github.com/ilabcode/pyhgf

공식 코드

pyhgf: A neural network library for predictive coding

https://github.com/ilia10000/SLkNN

공식 코드

'Less Than One'-Shot Learning: Learning N Classes From M<N Samples

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