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/annusha/lcwof

공식 코드

Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting

https://github.com/anonymous4545/jers

공식 코드

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

https://github.com/anonymouscode-computerscience/ride

공식 코드

RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars

https://github.com/anp-scp/fast_bci

공식 코드

Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface

https://github.com/anthonywchen/rarr

공식 코드

RARR: Researching and Revising What Language Models Say, Using Language Models

https://github.com/antonpuz/DeROL

공식 코드

Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

https://github.com/anugunjnaman/fixed-maml

공식 코드

Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition

https://github.com/anujinho/trident

공식 코드

Transductive Decoupled Variational Inference for Few-Shot Classification

https://github.com/anwarmaxsum/agla

공식 코드

Assessor-Guided Learning for Continual Environments

https://github.com/apivich-h/pied

공식 코드

PIED: Physics-Informed Experimental Design for Inverse Problems

https://github.com/apple2373/MetaIRNet

공식 코드

Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

https://github.com/applicaai/contract-discovery

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Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines

https://github.com/aquemy/DPSO_experiments

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Two-stage Optimization for Machine Learning Workflow

https://github.com/aravindr93/imaml_dev

공식 코드

Meta-Learning with Implicit Gradients

https://github.com/arghosh/RobustMW-Net

공식 코드

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

https://github.com/arielamar123/adc-learning-hyperopt

공식 코드

Deep Task-Based Analog-to-Digital Conversion

https://github.com/arjish/PreTrainedFullLibrary_FewShot

공식 코드

Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

https://github.com/arjish/meta-meta-classification

공식 코드

Meta-Meta Classification for One-Shot Learning

https://github.com/arnab39/FewShot_GAN-Unet3D

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Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

https://github.com/arnav0400/vit-slim

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One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

https://github.com/ars22/meta-learning-eval-id-vs-ood

공식 코드

Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

https://github.com/arvin0313/mecoin-gfscil

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An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning

https://github.com/aryahm1375/metagene

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Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection

https://github.com/asappresearch/structshot

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Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

https://github.com/ashaw596/meta_architecture_search

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Meta Architecture Search

https://github.com/ashengl/fsncd

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Few-shot Novel Category Discovery

https://github.com/ashiq24/coda-no

공식 코드

Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs

https://github.com/ashishp166/Noisy-Zero-Shot-Coordination

공식 코드

Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games

https://github.com/ashwinpathak20/idiomaticity_detection_using_few_shot_learning

공식 코드

JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

https://github.com/asrafulashiq/dynamic-cdfsl

공식 코드

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

https://github.com/astanic/crafter-ood

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Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter

https://github.com/atapour/ransomware-classification

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A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation

https://github.com/athulshibu/Rewarded-meta-pruning

공식 코드

Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning

https://github.com/atik666/representationtransfer

공식 코드

Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning

https://github.com/atmahou/promptslottagging

공식 코드

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

https://github.com/atulkum/pointer_summarizer

공식 코드

Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View

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