https://github.com/ruc-nlpir/flashrag
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/ruocwang/mixture-of-prompts
https://github.com/sabagh1994/code_dcf
https://github.com/sabeaussan/ROS_Unity
https://github.com/safpla/AutoLossRelease
AutoLoss: Learning Discrete Schedules for Alternate Optimization
https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net
https://github.com/sambanova/generative_data_prep
https://github.com/sambanova/lm-evaluation-harness
https://github.com/sambi97/Low-shot-learning_-Deep-learning-project
https://github.com/samsunglabs/myqasr
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
https://github.com/sanixa/CADA-VAE-pytorch
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
https://github.com/sanowar-raihan/nerf-meta
https://github.com/sarahesl/pubmedclip
Overcoming Data Limitation in Medical Visual Question Answering
https://github.com/sayakpaul/A-Barebones-Image-Retrieval-System
https://github.com/sayakpaul/FunMatch-Distillation
https://github.com/sayakpaul/PAWS-TF
https://github.com/sayef/fsner
https://github.com/schatty/matching-networks-tf
https://github.com/schatty/prototypical-networks-tf
https://github.com/schatty/siamese-networks-tf
https://github.com/scrayish/ML_NLP
https://github.com/sebamenabar/oaml-jax
https://github.com/seblee97/task_weighted_maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/sergiolib/reproduce_oml
https://github.com/servicenow/synbols
Synbols: Probing Learning Algorithms with Synthetic Datasets
https://github.com/seujung/SNAIL-gluon
https://github.com/sevro/torchmetal
https://github.com/shark-nlp/openicl
https://github.com/shawnbeaulieu/ANML
https://github.com/sheetalreddy/One-shot-learning
https://github.com/shin-ee-chen/bla
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/shiyunyi/meta-weight-net_code-optimization
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
https://github.com/shmsw25/factscore
https://github.com/shoutOutYangJie/Few-Shot-Adversarial-Learning-for-face-swap
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shruti-jadon/Covid-19-Detection-Few-Shot-Learning
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.