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Memorization

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코드

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

https://github.com/shenweichen/DeepCTR

Wide & Deep Learning for Recommender Systems

https://github.com/shenweichen/DeepCTR-Torch

Wide & Deep Learning for Recommender Systems

https://github.com/simongrest/farm-pin-crop-detection-challenge

mixup: Beyond Empirical Risk Minimization

https://github.com/simongrest/kaggle-freesound-audio-tagging-2019

mixup: Beyond Empirical Risk Minimization

https://github.com/smilelab-fl/fednoisy

mixup: Beyond Empirical Risk Minimization

https://github.com/srgrace/contextual-compression

In-context Autoencoder for Context Compression in a Large Language Model

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

Neural Machine Translation in Linear Time

https://github.com/stanl1y/med_TwoCycleLearning-and-MixUp

mixup: Beyond Empirical Risk Minimization

https://github.com/suzgunmirac/meta-prompting

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

https://github.com/swj0419/muse_bench

MUSE: Machine Unlearning Six-Way Evaluation for Language Models

https://github.com/tangxyw/RecAlgorithm

Wide & Deep Learning for Recommender Systems

https://github.com/tanmaypandey7/wheat-detection

mixup: Beyond Empirical Risk Minimization

https://github.com/teang1995/satellite_image_task

mixup: Beyond Empirical Risk Minimization

https://github.com/teddykoker/grokking

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

https://github.com/tensorflow/models/tree/master/research/lexnet_nc

Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds using Paraphrases in a Neural Model

https://github.com/thaihungle/UW-DNC

Learning to Remember More with Less Memorization

https://github.com/theophilee/learner-performance-prediction

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

https://github.com/theophilegervet/learner-performance-prediction

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

https://github.com/tml-epfl/os-harm

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

https://github.com/toshi-k/kaggle-bengaliai-handwritten-grapheme-classification

mixup: Beyond Empirical Risk Minimization

https://github.com/transcendentsky/mixup

mixup: Beyond Empirical Risk Minimization

https://github.com/tugstugi/pytorch-speech-commands

mixup: Beyond Empirical Risk Minimization

https://github.com/tusharip/augmentations

mixup: Beyond Empirical Risk Minimization

https://github.com/ucsb-nlp-chang/uld

Offset Unlearning for Large Language Models

https://github.com/viethungluu/co-teaching

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

https://github.com/vinaymittal22/Income_Prediction_US

Wide & Deep Learning for Recommender Systems

https://github.com/wangermeng2021/FastClassification

mixup: Beyond Empirical Risk Minimization

https://github.com/wrh14/deep_unlearning

MUSE: Machine Unlearning Six-Way Evaluation for Language Models

https://github.com/xingruiyu/coteaching_plus

How does Disagreement Help Generalization against Label Corruption?

https://github.com/xlang-ai/ds-1000

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

https://github.com/xue-pai/FuxiCTR

Wide & Deep Learning for Recommender Systems

https://github.com/yangling0818/buffer-of-thought-llm

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

https://github.com/yeachan-kr/pytorch-coteaching

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

https://github.com/yeachan-kr/pytorch-sigua

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

https://github.com/yell/kaggle-camera

mixup: Beyond Empirical Risk Minimization

https://github.com/yibeiou/Advanced_Business_Intelligence

Wide & Deep Learning for Recommender Systems

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