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Memorization

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jefferythewind/era-splitting-notebook-examples

Learning explanations that are hard to vary

https://github.com/jerermyyoung/rtlearning

A Closer Look at Memorization in Deep Networks

https://github.com/jianshen92/stanford-car-grab-challenge

mixup: Beyond Empirical Risk Minimization

https://github.com/jilljenn/ktm

DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills

https://github.com/jmaronas/calibration_MixupDNN_ARCLoss.pytorch

mixup: Beyond Empirical Risk Minimization

https://github.com/jonnor/datascience-master

mixup: Beyond Empirical Risk Minimization

https://github.com/js-aguiar/wheat-object-detection

mixup: Beyond Empirical Risk Minimization

https://github.com/jsleroux/Recommender-Systems

Wide & Deep Learning for Recommender Systems

https://github.com/jzhang38/tinyllama

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

https://github.com/kaistai/knowledge-entropy

How Do Large Language Models Acquire Factual Knowledge During Pretraining?

https://github.com/kbressem/longhealth

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

https://github.com/keyuchen886/GoodReads

Wide & Deep Learning for Recommender Systems

https://github.com/kindxiaoming/omnigrok

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

https://github.com/kingstarcraft/speech-to-text-wavenet2

Neural Machine Translation in Linear Time

https://github.com/kinimod23/ATS_Project

Neural Machine Translation in Linear Time

https://github.com/kli8996/HISR-Spectral-Mixup-Heterogeneous-Datasets

Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets

https://github.com/kornia/kornia

mixup: Beyond Empirical Risk Minimization

https://github.com/lRomul/argus-freesound

mixup: Beyond Empirical Risk Minimization

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Generalization through Memorization: Nearest Neighbor Language Models

https://github.com/layer6ai-labs/dgm_manifold_survey

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

https://github.com/leehomyc/mixup_pytorch

mixup: Beyond Empirical Risk Minimization

https://github.com/leolee99/osa

Negative Pre-aware for Noisy Cross-modal Matching

https://github.com/liguigui/speech-to-text-wavenet

Neural Machine Translation in Linear Time

https://github.com/lnstadrum/fastaugment

mixup: Beyond Empirical Risk Minimization

https://github.com/locuslab/open-unlearning

MUSE: Machine Unlearning Six-Way Evaluation for Language Models

https://github.com/louaaron/scaling-riemannian-diffusion

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

https://github.com/lucidrains/CoCa-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/PaLM-jax

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/PaLM-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucylow/Yeezy-Taught-Me

Grid Long Short-Term Memory

https://github.com/m42-health/med42

Capabilities of GPT-4 on Medical Challenge Problems

https://github.com/makeyourownmaker/mixup

mixup: Beyond Empirical Risk Minimization

https://github.com/marcuslassila/mia-audit-gnn

Leave-one-out Distinguishability in Machine Learning

https://github.com/massquantity/LibRecommender

Wide & Deep Learning for Recommender Systems

https://github.com/meabhishekkumar/iith_session_2020

Wide & Deep Learning for Recommender Systems

https://github.com/meghdad92/scrub

Towards Adversarial Evaluations for Inexact Machine Unlearning

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