1. Foundational Papers Of Machine Learning:

    ImageNet Classification with Deep Convolutional Neural Networks

    Densely Connected Convolutional Networks

    Fully Convolutional Networks for Semantic Segmentation

    Very Deep Convolutional Networks for Large-Scale Image Recognition

    Deep Residual Learning for Image Recognition

  2. Bioinformatics

    U-Net: Convolutional Networks for Biomedical Image Segmentation

    AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based DrugDiscovery

    Development and evaluation of a deep learning model for protein-ligand binding affinity prediction

  3. Autonomous Vehicles

    The Cityscapes Dataset for Semantic Urban Scene Understanding

    A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling

    Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection

  4. Intresting Miscellaneous Topics

    Deep Contrast Learning for Salient Object Detection

    Context-Aware Semantic Inpainting

    Colorful Image Colorization

This is not a comprehensive list of the papers I have read. To read a report of the what I learned from these and other papers look at my next blog post.