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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/pl97/fednlp

공식 코드

An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing

https://github.com/plai-group/bayes-pcn

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BayesPCN: A Continually Learnable Predictive Coding Associative Memory

https://github.com/plai-group/simple-cnaps

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Improved Few-Shot Visual Classification

https://github.com/pldlgb/MetaSD

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Self-Distillation with Meta Learning for Knowledge Graph Completion

https://github.com/pldlgb/nuggets

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One-Shot Learning as Instruction Data Prospector for Large Language Models

https://github.com/pmuellner/RobustnessOfMetaMF

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Robustness of Meta Matrix Factorization Against Strict Privacy Constraints

https://github.com/pnborchert/core

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CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation

https://github.com/pnborchert/multirep

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Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning

https://github.com/pnnl/pychip_gui

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Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

https://github.com/po-sheng/meta_transferring_for_deblurring

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Meta Transferring for Deblurring

https://github.com/polo5/fds

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Gradient-based Hyperparameter Optimization Over Long Horizons

https://github.com/premiertaco/premier-taco

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Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss

https://github.com/primebo1/FAMNet

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FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation

https://github.com/princeton-computational-imaging/gensdf

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GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions

https://github.com/princeton-nlp/LM-BFF

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Making Pre-trained Language Models Better Few-shot Learners

https://github.com/princeton-nlp/metric-wsd

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Non-Parametric Few-Shot Learning for Word Sense Disambiguation

https://github.com/prinshul/prototype_neuralode_adv_attack

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Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs

https://github.com/pris-cv/bi-frn

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Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

https://github.com/probcomp/solvingbib.jl

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Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind

https://github.com/probe2/kg_text

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Evaluating and Improving Graph to Text Generation with Large Language Models

https://github.com/prolearner/hypertorch

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On the Iteration Complexity of Hypergradient Computation

https://github.com/prolearner/nonsmooth_implicit_diff

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Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates

https://github.com/psacfc/gpt4ts_adapter

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Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

https://github.com/psandovalsegura/adversarialquerying

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AutoProtoNet: Interpretability for Prototypical Networks

https://github.com/pseulki/ib-loss

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Influence-Balanced Loss for Imbalanced Visual Classification

https://github.com/ptomaszewska/limes

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Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift

https://github.com/pulp-platform/eae-kws

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Boosting keyword spotting through on-device learnable user speech characteristics

https://github.com/pumpkinder/glip-cdfsod

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NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/punyajoy/rgfs_ecai

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Rationale-Guided Few-Shot Classification to Detect Abusive Language

https://github.com/purdueminds/isdea

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Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types

https://github.com/pxaris/fm-music-tagging

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Universal Music Representations? Evaluating Foundation Models on World Music Corpora

https://github.com/pxaris/lc-protonets

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LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging

https://github.com/pytorch/fairseq/tree/main/examples/xglm

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Few-shot Learning with Multilingual Language Models

https://github.com/qbetterk/DAML

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Domain Adaptive Dialog Generation via Meta Learning

https://github.com/qcpolimi/QA-MetaLearning

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Analyzing the Effectiveness of Quantum Annealing with Meta-Learning

https://github.com/qcri/llmebench

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LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking

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