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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/nikola310/Siamese-Neural-Networks

https://github.com/nixtla/transfer-learning-time-series

Meta-learning framework with applications to zero-shot time-series forecasting

https://github.com/nke001/causal_learning_unknown_interventions

Learning Neural Causal Models from Unknown Interventions

https://github.com/nku-hlt/diffeditor

CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

https://github.com/nlokeshiisc/pairnet_release

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

https://github.com/nlx-group/overlapy

Language Models are Few-Shot Learners

https://github.com/novicestone/meta-cetm

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ojedaf/TNT

TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification

https://github.com/ojiyumm/longbench_rwkv

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

https://github.com/omarfoq/knn-per

Exploiting Shared Representations for Personalized Federated Learning

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

https://github.com/opengptx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/opengvlab/cafo

Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners

https://github.com/openlamm/lamm

EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder

https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/oscarknagg/few-shot

Matching Networks for One Shot Learning

https://github.com/ovbystrova/InstructionNER

InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER

https://github.com/owen-yang-18/brainnn-pretrain

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

https://github.com/paperspapers/badprompt

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

https://github.com/pavliclab/neurips2023-unireps-hong-tgsh-randomly_weighted_neuromodulation_in_neural_networks

For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions

https://github.com/peisungtsai/Reptile-Pytorch-Implementation

On First-Order Meta-Learning Algorithms

https://github.com/pengbaolin/soloist

Few-shot Natural Language Generation for Task-Oriented Dialog

https://github.com/peter-yh-wu/xmodal

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

https://github.com/peymanbateni/simple-cnaps

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

https://github.com/pfnet-research/robust_estimation

Learning to Reweight Examples for Robust Deep Learning

https://github.com/phecy/SSL-FEW-SHOT

Self-Supervised Learning For Few-Shot Image Classification

https://github.com/philippe554/MANN

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/phoebussi/alpaca-cot

LLaMA: Open and Efficient Foundation Language Models

https://github.com/phucdev/TL_Bio_RE

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/phunterlau/paper_without_code/tree/main/examples/godel-agent

Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement

https://github.com/pjlab-adg/leapad

LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking

https://github.com/pku-ai-edge/roadnetsz

MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

https://github.com/pku-dair/hetu

PointCLIP: Point Cloud Understanding by CLIP

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.