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/uiuc-iml/autompc

공식 코드

Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning

https://github.com/ukplab/incorporating-relevance

공식 코드

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

https://github.com/umbertocappellazzo/petl_ast

공식 코드

Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers

https://github.com/uncbiag/easyreg

공식 코드

Anatomical Data Augmentation via Fluid-based Image Registration

https://github.com/unit8co/darts

공식 코드

Darts: User-Friendly Modern Machine Learning for Time Series

https://github.com/uobinxiao/semantic_cross_attention_fsl

공식 코드

Semantic Cross Attention for Few-shot Learning

https://github.com/usaito/asymmetric-tri-rec-real

공식 코드

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback

https://github.com/usc-gvl/agent-driver

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A Language Agent for Autonomous Driving

https://github.com/usnistgov/NIST-AI-Meta-Learning-LLM

공식 코드

Meta learning with language models: Challenges and opportunities in the classification of imbalanced text

https://github.com/ustc-starteam/chemeval

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ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models

https://github.com/ustc-starteam/td3

공식 코드

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation

https://github.com/ut-austin-rpl/amago

공식 코드

AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents

https://github.com/utcsilab/fewmax

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Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

https://github.com/utkarshnath/Adjoint-Network

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Adjoined Networks: A Training Paradigm with Applications to Network Compression

https://github.com/uts-caslab/autoweka

공식 코드

On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning

https://github.com/uvm-neurobotics-lab/ANML

공식 코드

Learning to Continually Learn

https://github.com/valeoai/BF3S

공식 코드

Boosting Few-Shot Visual Learning with Self-Supervision

https://github.com/vanderschaarlab/mlforhealthlabpub

공식 코드

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms

https://github.com/vbeliveau/radiology-text-classification

공식 코드

Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language

https://github.com/vcl-iisc/robust-few-shot-learning

공식 코드

Robust Few-shot Learning Without Using any Adversarial Samples

https://github.com/vdutor/neural-diffusion-processes

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Neural Diffusion Processes

https://github.com/vgsatorras/few-shot-gnn

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Few-Shot Learning with Graph Neural Networks

https://github.com/vico-uoe/universalrepresentations

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Universal Representations: A Unified Look at Multiple Task and Domain Learning

https://github.com/vihari/csd

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Efficient Domain Generalization via Common-Specific Low-Rank Decomposition

https://github.com/vinairesearch/mdsdi

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Exploiting Domain-Specific Features to Enhance Domain Generalization

https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer

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Enhancing Few-shot Image Classification with Cosine Transformer

https://github.com/vinuni-vishc/pppf-cross-silo-fl

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Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning

https://github.com/vios-s/DGNet

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Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation

https://github.com/vis-opt-group/bda

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A General Descent Aggregation Framework for Gradient-based Bi-level Optimization

https://github.com/vision-kek/abcdfss

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

https://github.com/visionml/pytracking

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Learning What to Learn for Video Object Segmentation

https://github.com/vita-group/layergraftedpretraining_iclr23

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Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations

https://github.com/vitae-transformer/remote-sensing-rvsa

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Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model

https://github.com/vitae-transformer/vitae-transformer-remote-sensing

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Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model

https://github.com/vityavitalich/taxollama

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TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

https://github.com/vivekmyers/vmgp

공식 코드

Bayesian Meta-Learning Through Variational Gaussian Processes

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