Reading-ML/DL_Papers: CV + Medical Image Analysis

• What's Hidden in a Randomly Weighted Neural Network? [29 Nov 2019] [arXiv]

CVPR2022 Histopathology Images

• Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
• Cross-patch Dense Contrastive Learning for Semi-supervised Segmentation of Cellular Nuclei in Histopathologic Images
• Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification
• Cell Selection-Based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia
• (WS) Multi-Class Cell Detection Using Modified Self-Attention

Symbolic Computation

• Deep Learning for Symbolic Mathematics [2 Dec 2019] [arXiv]

Data preprocessing for Histopathology Image

• [Otsu] Deep Learning for Identifying Metastatic Breast Cancer (2017) [arXiv]
• [Otsu] A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images (29 Jan 2019) [paper]

Multi-task

• Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (May 2017) [arXiv]
• Cross-stitch Networks for Multi-task Learning (Apr 2016) [arXiv]

GAN

• A Review on Generative Adversarial Networks:Algorithms, Theory, and Applications (20 Jan 2020) [arXiv]
• GAN_Review [Github]

Pytorch

• PyTorch: An Imperative Style, High-Performance Deep Learning Library (3 Dec 2019) [arXiv]

Efficent deep learning

• SqueezeNext: Hardware-Aware Neural Network Design (March 2018) [arXiv]

Others

• Reviving and Improving Recurrent Back-Propagation (March 2018) [arXiv]
• A scikit-learn compatible neural network library that wraps pytorch [github]

Other awsome lists

• GANs in general computer vision [github]
• GANs for Medical Imaging [github]
• Knowlege Distillation [github]
• Knowlege Distillation 2 [github]

Tutorial page

• ML [link]
• Notebook [github]
• ML colection [link]
• Deep Learning on Graphs CMU [link]

CS294-158 Deep Unsupervised Learning

• GAN, DC GAN, ImprovedGAN, WGAN, WGAN-GP, Progr.GAN, SN-GAN, SAGAN, BigGAN(-Deep), StyleGAN-v1,2, VIB-GAN, GANs as Energy Models Abbeel et al. Video: https://youtu.be/1CT-kxjYbFU site: https://sites.google.com/view/berkeley-cs294-158-sp20/home

RL

• UCL Course on RL (DAVID SILVER): https://www.davidsilver.uk/teaching/

• CS 294-112 at UC Berkeley: Deep Reinforcement Learning: 2018: http://rail.eecs.berkeley.edu/deeprlcourse-fa18/ solution: https://github.com/daggertye/CS294_homework

• stanford-ai-courses: https://ai.stanford.edu/courses/?fbclid=IwAR1RdWVDMK8YDsGqvR4cTj6dDHE99EZYvMuCgFXHjdp4ug0hmVUq92mmXTk

• Deep Reinforcement Learning: CS 285 at UC Berkeley, Fall 2020: Levine et al.: [YouTube]

• Graph Representation Learning https://paperswithcode.com/task/graph-representation-learning

• Graph Representation Learning by William L. Hamilton, McGill University [Book]

• Deep Learning on Graphs Michigan State University [Web]

DL Math

• Mathematical Foundations of Machine Learning (Fall 2020) Rebecca Willett, University of Chicago [web]