kwoongbae / deep-learning-papers

Deep Learning papers reviewed by @kwoongbae (diffusion-model, xai, nlp)

Overview

• 이 레포는 딥러닝/머신러닝과 관련된 다양한 자료, 논문들을 정리하여 요약 및 리뷰를 하는 모음입니다.

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Paper reviews

• Attention is All you need (NeurIPS 2017) link / ppt
• Transformer Networks for trajectory forecasting (ICPR 2021) link / ppt

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Academic materials

• Top-tier AI conference (Link)

  • CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition / Seattle on 2024)
  • ICLR (International Conference on Learning Representations / Austria)
  • NeurIPS (Neural Information Processing Systems / New Orleans)
  • ICML (International Conference on Machine Learning / Austria)
  • ECCV (European Conference on Computer Vision / Milano)
  • AAAI (Association for the Advancement of Artificial Intelligence / Vancouver)
  • ACL (Association for Computational Linguistics / Thailand)
  • IJCAI (International Joint Conference on Artificial Intelligence / Jeju island)
  • KDD (ACM International Conference on Knowledge Discovery and Data Mining / Barcelona)
  • Interspeech (International Speech Communication Association / Greece)

deep-learning-papers's Issues

Tutorial on Diffusion Models for Imaging and Vision

• https://arxiv.org/abs/2403.18103
• CHAN, Stanley H. Tutorial on Diffusion Models for Imaging and Vision. arXiv preprint arXiv:2403.18103, 2024.

Do Transformers Really Perform Bad for Graph Representation?

• https://arxiv.org/abs/2106.05234

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

• https://arxiv.org/abs/2006.09252

On the Constrained Time-Series Generation Problem

• https://proceedings.neurips.cc/paper_files/paper/2023/hash/bfb6a69c0d9e2bc596e1cd31f16fcdde-Abstract-Conference.html
• COLETTA, Andrea, et al. On the constrained time-series generation problem. Advances in Neural Information Processing Systems, 2024, 36.

Denoising Diffusion Probabilistic Models

• https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
• HO, Jonathan; JAIN, Ajay; ABBEEL, Pieter. Denoising diffusion probabilistic models. Advances in neural information processing systems, 2020, 33: 6840-6851.

Deep Image Generative Models

• https://www.youtube.com/watch?v=oq7lZ_JwX5M&t=2082s
• 융합과학기술대학원 지능정보융합학과 곽노준 교수

Understanding over-squashing and bottlenecks on graphs via curvature

• https://arxiv.org/abs/2111.14522