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/meghdadk/SCRUB

Towards Adversarial Evaluations for Inexact Machine Unlearning

https://github.com/microsoft/protein-sequence-models

Neural Machine Translation in Linear Time

https://github.com/microsoft/recommenders

Wide & Deep Learning for Recommender Systems

https://github.com/mindspore-ai/models/tree/master/official/recommend/wide_and_deep

Wide & Deep Learning for Recommender Systems

https://github.com/mingsun-tse/good-da-in-kd

mixup: Beyond Empirical Risk Minimization

https://github.com/mohammadpz/Associative_LSTM

Associative Long Short-Term Memory

https://github.com/mzhaoshuai/slimclr

Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training

https://github.com/nebyu08/tiny_recommendation

Wide & Deep Learning for Recommender Systems

https://github.com/neulab/knn-transformers

Generalization through Memorization: Nearest Neighbor Language Models

https://github.com/nikolaev-n/CocktailPartySpeakerRecognition

mixup: Beyond Empirical Risk Minimization

https://github.com/nlbse2024/code-comment-classification

The (ab)use of Open Source Code to Train Large Language Models

https://github.com/ntucllab/climage_dataset

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

https://github.com/ntucllab/complementary_cifar

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

https://github.com/opengvlab/internvideo2

VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling

https://github.com/paarthneekhara/byteNet-tensorflow

Neural Machine Translation in Linear Time

https://github.com/pollyyu/Final_Project_MachineLearning_in_TensorFlow_Berkeley

Wide & Deep Learning for Recommender Systems

https://github.com/psr007ps/NeuralNetAugmentations

mixup: Beyond Empirical Risk Minimization

https://github.com/pytorch/vision

mixup: Beyond Empirical Risk Minimization

https://github.com/qiuqiu1602/Pointer_Value_Retrieval

Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization

https://github.com/qmonmous/BigData-X-Python

Wide & Deep Learning for Recommender Systems

https://github.com/qmonmous/pySpark-X-RecommenderSys--intro

Wide & Deep Learning for Recommender Systems

https://github.com/randomrandom/deep-atrous-cnn-sentiment

Neural Machine Translation in Linear Time

https://github.com/rit-git/Snippext_public

mixup: Beyond Empirical Risk Minimization

https://github.com/roeeaharoni/sprp-acl2018

Split and Rephrase: Better Evaluation and a Stronger Baseline

https://github.com/roozbeh-yz/similarities

Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets

https://github.com/rucaibox/simpledeepsearcher

R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning

https://github.com/rvlopes/gloria

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

https://github.com/rwightman/pytorch-image-models

mixup: Beyond Empirical Risk Minimization

https://github.com/ryanwebster90/gen-overfitting-latent-recovery

Detecting Overfitting of Deep Generative Networks via Latent Recovery

https://github.com/sailist/thexp-implement

mixup: Beyond Empirical Risk Minimization

https://github.com/sandeepnair2812/Deep-Learning-Based-Search-and-Recommendation-System

Wide & Deep Learning for Recommender Systems

https://github.com/sayakpaul/FunMatch-Distillation

mixup: Beyond Empirical Risk Minimization

https://github.com/scut-aitcm/Competitive-Inner-Imaging-SENet

mixup: Beyond Empirical Risk Minimization

https://github.com/scut-aitcm/CompetitiveSENet

mixup: Beyond Empirical Risk Minimization

https://github.com/shahriargolchin/time-travel-in-llms

Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models

https://github.com/shengliu66/sop

Early-Learning Regularization Prevents Memorization of Noisy Labels

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