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🍃Paper Reading

This repo contains my paper reading notes on deep learning. Check the notes at https://bc-li.github.io/paperreading.

Paper reading notes

PHASE #1

Title Field Time Report link Time I started Status
[ICCV 2015] Learning Deconvolution Network for Semantic Segmentation VISION 2015 https://bc-li.github.io/paper/deconvnet 2021/5/17 Done
[NeurIPS 2017] Attention Is All You Need NLP 2017 https://bc-li.github.io/paper/transformer 2021/12/11 Done
[NAACL 2019] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding NLP 2018 https://bc-li.github.io/paper/bert 2021/12/15 Done
[NeurIPS 2014] Sequence to Sequence Learning with Neural Networks NLP 2014 https://bc-li.github.io/paper/seq2seq 2022/1/21 Done
[ICLR 2018] Non-Autoregressive Neural Machine Translation NLP 2018 https://bc-li.github.io/paper/nonauto 2022/1/24 Done
[ICLR 2019] Parameter-Efficient Transfer Learning for NLP NLP 2019 https://bc-li.github.io/paper/petl 2022/2/2 Done
[ICLR 2018] Unsupervised Neural Machine Translation NLP 2018 https://bc-li.github.io/paper/unsupervised-NMT 2022/2/4 Done

PHASE #1.5

Title Field Time Report link Time I started Status
Classic CNN structures (LeNet to DenseNet) VISION 1998-2017 https://bc-li.github.io/paper/cnn 2022/2/25 Done
MobileNets series [V1 to V3] VISION 2017 https://bc-li.github.io/paper/mobilenets 2022/3/10 Done
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size VISION 2016 https://bc-li.github.io/paper/SqueezeNet 2022/3/10 Pending

Classic CNN structures: 对近年来比较经典的 CNN 架构类型做了小结,并基于 d2l.ai 提供代码在 FASHION 数据集上进行训练,做了简单尝试。

PHASE #2

Title Field Time Report link Time I started Status
[NeurIPS 2019] Levenshtein Transformer NLP 2019 https://bc-li.github.io/paper/lt 2022/2/15 Pending

Stack

Title Field Time Report link Time I started Status
[SCTS 2020] Pre-trained Models for Natural Language Processing: A Survey NLP 2020 N/A N/A N/A

写 blog 的时候如未特殊说明则为从约为零基础开始。在 blog post 中我会把我为了理解文中一些比较 specific 的概念找到的相对容易理解的原出处贴到文中,方便查阅,且不再重复阐述。

Acknowledgements

https://github.com/mli/paper-reading

https://www.deeplearningbook.org/