Papers with Code 아카이브

Meta-Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/mims-harvard/g-meta

공식 코드

Graph Meta Learning via Local Subgraphs

https://github.com/mindflow-institue/annotation_free_fewshot

공식 코드

Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach

https://github.com/mindspore-ai/mindscience/tree/master/MindSPONGE/applications/MEGAProtein

공식 코드

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

https://github.com/mingchenli/VGAE_Attack_Meta

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Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction

https://github.com/minglllli/CLS-RL

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Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning

https://github.com/minhquanlecs/maskdiff

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MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

https://github.com/minhtcai/MLMF

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3FM: Multi-modal Meta-learning for Federated Tasks

https://github.com/minimario/morbit

공식 코드

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning

https://github.com/minnesotanlp/eyestyliency

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A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models

https://github.com/minnesotanlp/mpo

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Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models

https://github.com/minusadd/meta-learning-parameterized-skills

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Meta-Learning Parameterized Skills

https://github.com/minyoungkim21/niwmeta

공식 코드

A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning

https://github.com/mir-mu/coat

공식 코드

Can In-context Learners Learn a Reasoning Concept from Demonstrations?

https://github.com/mit-ll/robust-vision-language-finetuning

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Robust Fine-Tuning of Vision-Language Models for Domain Generalization

https://github.com/mkashifali/metavideostab

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Harnessing Meta-Learning for Improving Full-Frame Video Stabilization

https://github.com/mkhodak/ARUBA

공식 코드

Adaptive Gradient-Based Meta-Learning Methods

https://github.com/mkhodak/FMRL

공식 코드

Provable Guarantees for Gradient-Based Meta-Learning

https://github.com/ml-jku/chef

공식 코드

Cross-Domain Few-Shot Learning by Representation Fusion

https://github.com/ml-jku/clamp

공식 코드

Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

https://github.com/ml-jku/mhnfs

공식 코드

Context-enriched molecule representations improve few-shot drug discovery

https://github.com/ml-jku/subgd

공식 코드

Few-Shot Learning by Dimensionality Reduction in Gradient Space

https://github.com/ml-postech/metassd

공식 코드

MetaSSD: Meta-Learned Self-Supervised Detection

https://github.com/ml4ai/mliis

공식 코드

Meta-Learning Initializations for Image Segmentation

https://github.com/mlaurelli/amdtl

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Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI

https://github.com/mlfoundations/open_clip

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LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

https://github.com/mlvlab/SELAR

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Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

https://github.com/mlvlab/rpo

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Read-only Prompt Optimization for Vision-Language Few-shot Learning

https://github.com/mmcdermott/comprehensive_MTL_EHR

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A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data

https://github.com/mmoradi-iut/BioGPT-3

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GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain

https://github.com/mogicianxd/resus

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RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction

https://github.com/mohammadJaliliTorkamani/Kajal

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Kajal: Extracting Grammar of a Source Code Using Large Language Models

https://github.com/mohammedsb/dinov2formedical

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Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

https://github.com/mohammedsb/dinov2forradiology

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Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

https://github.com/mohna0310/zssc

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Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts

https://github.com/moisesrsantos/featselec

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Evaluating Meta-Feature Selection for the Algorithm Recommendation Problem

https://github.com/mominabbass/sharp-maml

공식 코드

Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning

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