Paper-Reading

NeurIPS 2017:

1. Attention Is All You Need
   paper:https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

CVPR 2020:

1. Data Uncertainty Learning in Face Recognition
   paper:https://openaccess.thecvf.com/content_CVPR_2020/papers/Chang_Data_Uncertainty_Learning_in_Face_Recognition_CVPR_2020_paper.pdf
2. Variational Neural Network Pruning
   paper:https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Variational_Convolutional_Neural_Network_Pruning_CVPR_2019_paper.pdf

NeurIPS 2020:

1. Uncertainty Aware Semi-Supervised Learning on Graph Data
   presentation:https://nips.cc/virtual/2020/protected/poster_968c9b4f09cbb7d7925f38aea3484111.html paper:https://proceedings.neurips.cc/paper/2020/file/968c9b4f09cbb7d7925f38aea3484111-Paper.pdf
2. Uncertainty-aware Self-training for Few-shot Text Classification
   presentation:https://nips.cc/virtual/2020/protected/poster_f23d125da1e29e34c552f448610ff25f.html paper:https://proceedings.neurips.cc/paper/2020/file/f23d125da1e29e34c552f448610ff25f-Paper.pdf
3. PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
   presentation:https://nips.cc/virtual/2020/protected/poster_8fb134f258b1f7865a6ab2d935a897c9.html paper:https://proceedings.neurips.cc/paper/2020/file/8fb134f258b1f7865a6ab2d935a897c9-Paper.pdf
4. Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings presentation:https://nips.cc/virtual/2020/protected/poster_d882050bb9eeba930974f596931be527.html
   paper:https://proceedings.neurips.cc/paper/2020/file/d882050bb9eeba930974f596931be527-Paper.pdf
5. Random Walk Graph Neural Networks presentation:https://nips.cc/virtual/2020/protected/poster_ba95d78a7c942571185308775a97a3a0.html paper:https://proceedings.neurips.cc/paper/2020/file/ba95d78a7c942571185308775a97a3a0-Paper.pdf
6. Graph Random Neural Networks for Semi-Supervised Learning on Graphs
   presentation:https://nips.cc/virtual/2020/protected/poster_fb4c835feb0a65cc39739320d7a51c02.html paper:https://proceedings.neurips.cc/paper/2020/file/fb4c835feb0a65cc39739320d7a51c02-Paper.pdf
7. Factorizable Graph Convolutional Networks
   presentation:https://nips.cc/virtual/2020/protected/poster_ea3502c3594588f0e9d5142f99c66627.html
   paper:https://proceedings.neurips.cc/paper/2020/file/ea3502c3594588f0e9d5142f99c66627-Paper.pdf
8. Building Powerful and Equivariant Graph Neural Networks with Structural Message-passing
   presentation:https://nips.cc/virtual/2020/protected/poster_a32d7eeaae19821fd9ce317f3ce952a7.html
   paper:https://proceedings.neurips.cc/paper/2020/file/a32d7eeaae19821fd9ce317f3ce952a7-Paper.pdf
9. Bayesian Attention Module
   presentation:https://nips.cc/virtual/2020/protected/poster_bcff3f632fd16ff099a49c2f0932b47a.html
   paper:https://proceedings.neurips.cc/paper/2020/file/bcff3f632fd16ff099a49c2f0932b47a-Paper.pdf
10. Auto Learning Attention
    presentation:https://nips.cc/virtual/2020/protected/poster_103303dd56a731e377d01f6a37badae3.html paper:https://proceedings.neurips.cc/paper/2020/file/103303dd56a731e377d01f6a37badae3-Paper.pdf
11. Implicit Graph Neural Networks
    presentation:https://nips.cc/virtual/2020/protected/poster_8b5c8441a8ff8e151b191c53c1842a38.html
    paper:https://proceedings.neurips.cc/paper/2020/file/8b5c8441a8ff8e151b191c53c1842a38-Paper.pdf
12. Pointer Graph Networks
    presentation:https://nips.cc/virtual/2020/protected/poster_176bf6219855a6eb1f3a30903e34b6fb.html
    paper:https://proceedings.neurips.cc/paper/2020/file/176bf6219855a6eb1f3a30903e34b6fb-Paper.pdf

FER paper:

1. Suppressing Uncertainties for Large-Scale Facial Expression Recognition
   paper:https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Suppressing_Uncertainties_for_Large-Scale_Facial_Expression_Recognition_CVPR_2020_paper.pdf

AAAI 2021:

1. UAG: Uncertainty-Aware Attention Graph Neural Network for Defending Adversarial Attacks
   paper:https://virtual.2021.aaai.org/paper_AAAI-447.html
2. Uncertainty-Matching Graph Neural Networks to Defend against Poisoning Attacks
   paper:https://virtual.2021.aaai.org/paper_AAAI-4382.html

Transformer:

1. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows paper:https://arxiv.org/abs/2103.14030

Diffusion model：

1. Diffusion Models: A Comprehensive Survey of Methods and Applications
   paper:https://arxiv.org/abs/2209.00796

2. Image Super-Resolution via Iterative Refinement
   https://arxiv.org/pdf/2104.07636.pdf

3. Denoising Diffusion Probabilistic Models
   https://arxiv.org/pdf/2006.11239.pdf

4. High-Resolution Image Synthesis with Latent Diffusion Models
   https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf
   https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rombach_High-Resolution_Image_Synthesis_CVPR_2022_supplemental.pdf

5. HS-Diffusion: Learning a Semantic-Guided Diffusion Model for Head Swapping
   https://arxiv.org/pdf/2212.06458.pdf

6. Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
   https://mstypulkowski.github.io/diffusedheads/

7. Cascaded Diffusion Models for High Fidelity Image Generation
   https://cascaded-diffusion.github.io/assets/cascaded_diffusion.pdf