underthelights / paperarxiv

Papers for Deep Learning

Kyuhwan Shim 🤗

• Undergraduate Research Intern @ the BI Lab, Seoul Nat'l University
• Senior Undergraduate majoring Computer Science 💻 @ Sogang University
• Previously,
  • ML Engineer Intern @ Nota Inc., Seoul, Korea (23.03 ~ 23.08)
  • Designer @ OCONNECT, Ulsan, Korea (20.06 ~ 23.06)
• Links
  • E-mail / Curriculum Vitae/ 📄 GitBlog

paperarxiv's Issues

Neural Net Arch Genealogy

Neural Net Arch Genealogy
Reinforcement Learning Algorithms
A3C, '16.02.06
DARLA, '17.07.26
ACTKR, '17.08.17
c51, '17.10.27
CNN
AlexNet, '12.12
VggNet, '14.09
GoogLeNet, '14.09
ResNet, '15.12
DenseNet, '16.08
SENet: Squeeze-and-Excitation Networks, '17.09
Object Detection
R-CNN
Fast R-CNN
Faster R-CNN
Mask R-CNN
YOLO
SSD
R-FCN
Semantic Segmentation
FCN
DeconvNet
DeepLab
U-Net
Super-resolution
MemNet
FSRCNN
SRCNN
VDSR
DRCN
LabSRN
EDSR
TTS
Wavenet, '16.09.12
Generative Models
Autoregressive models
MADE, '15.02.12
PixelRNN, '16.01.25
NADE, '16.05.07
PixelCNN, '16.06.16
PixelCNN++, '17.01.19
Latent variable models
VAE, '13.12.20
CVAE, '14.06.20
AAE, '15.11.18
AVB, '17.01.17
VQ-VAE, '17.11.2
GAN, '14.06.10
Variants
CGAN, '14.11.06
DCGAN, '15.11.19
infoGAN, '16.06.12
EBGAN, '16.09.11
ACGAN, '16.10.30
WGAN, '17.01.26
BEGAN, '17.02.27
WGAN-GP, '17.03.31
TripleGAN, '17.03.07
Applications
Pix2Pix, '16.11.21
PPGN, '16.11.30
StackGAN, '16.12.10
RNN
LSTM, '97.11
GRU, 14.11
ACT: Adaptive Computation Time, '17.05
S2S: RNN Encoder-Decoder, '14.06
Attention: Jointly Learning to Align, '14.09
Effective Approaches to Attention, Luong et al. '15.08
DCN: Dynamic Coattention Networks, '16.08, DCN+, '17.08
Self-attention
NLP
Transformer: Attention Is All You Need, '17.06
GPT1: Improving Language Understanding by Generative Pre-Training, '18.06
BERT: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, '18.10
GPT2: Language Models are Unsupervised Multitask Learners, '19.01
GPT3: Language Models are Few-Shot Learners, '20.05
Vision
Image-GPT: Generative Pretraining from Pixels, '20.06
Vision transformer: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, '20.09
DeiT: Training data-efficient image transformers & distillation through attention, '20.12
Speech
Multimodal
DALL-E, '21.01
CLIP: Learning Transferable Visual Models From Natural Language Supervision

[Capsule Net, '17.10](https://arxiv.org/abs/1710.09829)
Memory Networks
	Neural Programming
		[Neural Turing Machine,'14.10](https://arxiv.org/pdf/1410.5401.pdf)
		[Neural Random-Access Machines,'16.02](https://arxiv.org/pdf/1511.06392.pdf)
		[Hierarchical Attentive Memory, '16.02](https://arxiv.org/abs/1602.03218)
		[Neural GPUs Learn Algorithms, '16.03](https://arxiv.org/pdf/1511.08228.pdf)
		[Neural Programmer,'16.08](https://arxiv.org/pdf/1511.04834.pdf)
		[Neural Module Networks, '16.06](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Andreas_Neural_Module_Networks_CVPR_2016_paper.html)
		[Hybrid Computing, '16.10](https://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)
	[Memory Networks,'14.10](https://arxiv.org/pdf/1410.3916.pdf)
	[End-to-End Memory Network,'15.03](https://arxiv.org/pdf/1503.08895.pdf)
	[DMN: Dynamic Memory Network, '16.03](https://arxiv.org/pdf/1506.07285.pdf), [DMN+, '16.04 ](https://arxiv.org/pdf/1603.01417.pdf)