Papers with Code 아카이브

Memorization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

1

보존된 평가 기록

3

코드

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

https://github.com/RhincodonE/Privacy-Onion-effect

The Privacy Onion Effect: Memorization is Relative

https://github.com/RohitDhankar/kaggle_wheat

mixup: Beyond Empirical Risk Minimization

https://github.com/Ryoo72/dimension-wise_mixup

mixup: Beyond Empirical Risk Minimization

https://github.com/SarthakYadav/audax

mixup: Beyond Empirical Risk Minimization

https://github.com/Sea-Snell/grokking

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

https://github.com/Shivendra-psc/speechbot

Neural Machine Translation in Linear Time

https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning

mixup: Beyond Empirical Risk Minimization

https://github.com/Taoudi/DataAugmentation

mixup: Beyond Empirical Risk Minimization

https://github.com/Taoudi/ImbalancedData

mixup: Beyond Empirical Risk Minimization

https://github.com/UlionTse/mlgb

Wide & Deep Learning for Recommender Systems

https://github.com/Vikas-Sony/speech-to-text

Neural Machine Translation in Linear Time

https://github.com/Westlake-AI/openmixup

mixup: Beyond Empirical Risk Minimization

https://github.com/Yangget/Mixup_All-use

mixup: Beyond Empirical Risk Minimization

https://github.com/abmitra84/recommender_system

Wide & Deep Learning for Recommender Systems

https://github.com/acmiyaguchi/birdclef-2022

mixup: Beyond Empirical Risk Minimization

https://github.com/adityaagrawal7/speech-to-text-wavenet

Neural Machine Translation in Linear Time

https://github.com/ag1988/dss

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers

https://github.com/aithlab/ImprovingGate

Improving the Gating Mechanism of Recurrent Neural Networks

https://github.com/aivolcano/RecSys_tf2

Wide & Deep Learning for Recommender Systems

https://github.com/aj9011/Wide-and-Deep

Wide & Deep Learning for Recommender Systems

https://github.com/aj9011/Wide-and-Deep-Demand-Forecasting

Wide & Deep Learning for Recommender Systems

https://github.com/akjgskwi/mixup_chainer

mixup: Beyond Empirical Risk Minimization

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/wide_and_deep.py

Wide & Deep Learning for Recommender Systems

https://github.com/alibaba/TorchEasyRec

Wide & Deep Learning for Recommender Systems

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/alsoj/Recommenders-movielens

Wide & Deep Learning for Recommender Systems

https://github.com/amerch/CIFAR100-Training

mixup: Beyond Empirical Risk Minimization

https://github.com/andychinka/dcase-challenge

mixup: Beyond Empirical Risk Minimization

https://github.com/avitalsh/reconst_based_MIA

Membership Inference Attacks are Easier on Difficult Problems

https://github.com/awslabs/sagemaker-privacy-for-nlp

Auditing Data Provenance in Text-Generation Models

https://github.com/bastianwandt/RepNet

RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation

https://github.com/ben-davidson-6/fixup

mixup: Beyond Empirical Risk Minimization

https://github.com/bhanML/coteaching_plus

How does Disagreement Help Generalization against Label Corruption?

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/bytedance/largebatchctr

Wide & Deep Learning for Recommender Systems

https://github.com/carsonyang518/aaai24-aes-afg

SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring

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