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/songlei00/ribbo

공식 코드

Reinforced In-Context Black-Box Optimization

https://github.com/songw-sw/cosmic

공식 코드

Contrastive Meta-Learning for Few-shot Node Classification

https://github.com/songw-sw/f2l

공식 코드

Federated Few-shot Learning

https://github.com/songw-sw/faith

공식 코드

FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

https://github.com/songw-sw/glitter

공식 코드

Graph Few-shot Learning with Task-specific Structures

https://github.com/songw-sw/tent

공식 코드

Task-Adaptive Few-shot Node Classification

https://github.com/songw-sw/x-fnc

공식 코드

Few-shot Node Classification with Extremely Weak Supervision

https://github.com/soochan-lee/sb-mcl

공식 코드

Learning to Continually Learn with the Bayesian Principle

https://github.com/sophiewharrie/causal-similarity-based-hierarchical-bayesian-models

공식 코드

Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms

https://github.com/sophiewharrie/meta-learning-hierarchical-model-similar-causal-mechanisms

공식 코드

Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms

https://github.com/sorobedio/dnnwg

공식 코드

Diffusion-Based Neural Network Weights Generation

https://github.com/sowmaster/esjacobians

공식 코드

On the Convergence Theory for Hessian-Free Bilevel Algorithms

https://github.com/spacefarers/metainr

공식 코드

Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural Representation

https://github.com/spearsheep/APT-Architectural-Planning-LLM-Agent

공식 코드

APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents

https://github.com/spiglerg/pyMeta

공식 코드

Meta-learnt priors slow down catastrophic forgetting in neural networks

https://github.com/spraise/BSNet

공식 코드

https://github.com/srebuffi/revisiting_saliency

공식 코드

There and Back Again: Revisiting Backpropagation Saliency Methods

https://github.com/sreejank/Abstract_Neural_Metamers

공식 코드

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

https://github.com/sreejank/Compositional_MetaRL

공식 코드

Meta-Learning of Structured Task Distributions in Humans and Machines

https://github.com/sreejank/language_and_programs

공식 코드

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

https://github.com/sriprabhar/km-maml

공식 코드

Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks

https://github.com/srvcodes/neural-processes-survey

공식 코드

The Neural Process Family: Survey, Applications and Perspectives

https://github.com/srzer/LaMo-2023

공식 코드

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

https://github.com/stanek-fi/M6_article

공식 코드

Designing Time-Series Models With Hypernetworks & Adversarial Portfolios

https://github.com/stanfordmlgroup/MedSelect

공식 코드

MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning

https://github.com/start2020/awesome-timeseries-llm-fm

공식 코드

A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model

https://github.com/stefanosantaris/melanie

공식 코드

Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events

https://github.com/stefanowoerner/medimeta-fsl-benchmark

공식 코드

Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging

https://github.com/stellatogrp/datadrivenoptimizerguarantees

공식 코드

Data-Driven Performance Guarantees for Classical and Learned Optimizers

https://github.com/stepjam/RLBench

공식 코드

RLBench: The Robot Learning Benchmark & Learning Environment

https://github.com/stepjam/TecNets

공식 코드

Task-Embedded Control Networks for Few-Shot Imitation Learning

https://github.com/steremma/curr-meta-learning

공식 코드

Curriculum Meta-Learning for Few-shot Classification

https://github.com/stomachcold/hctransformers

공식 코드

Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning

https://github.com/stypoumic/beclr

공식 코드

BECLR: Batch Enhanced Contrastive Few-Shot Learning

https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise

공식 코드

Do We Really Need Gold Samples for Sample Weighting Under Label Noise?

https://github.com/sudraj2002/fsgrasp

공식 코드

Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning

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