https://github.com/aksub99/dl-papers

Papers I am currently reading/implementing or have read/implemented.
https://github.com/aksub99/dl-papers

computer-vision deep-learning notes

Last synced: 6 months ago
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Papers I am currently reading/implementing or have read/implemented.

• Host: GitHub
• URL: https://github.com/aksub99/dl-papers
• Owner: aksub99
• Created: 2019-01-15T13:01:15.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-21T14:34:37.000Z (over 6 years ago)
• Last Synced: 2025-02-12T20:17:22.230Z (8 months ago)
• Topics: computer-vision, deep-learning, notes
• Homepage:
• Size: 1.22 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Papers I have read and/or implemented:

1)Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces  

  https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.146401  

  

2)Cross-language framework for word recognition and spotting of Indic scripts  

  https://www.sciencedirect.com/science/article/pii/S0031320318300463#bib0020  

3)U-Net: Convolutional Networks for Biomedical Image Segmentation  

  https://arxiv.org/abs/1505.04597  

4)TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation  

  https://arxiv.org/abs/1801.05746  

5)Feature Pyramid Networks for Object Detection  

  https://arxiv.org/abs/1612.03144  

6)Focal Loss for Dense Object Detection  

  https://arxiv.org/abs/1708.02002  

7)MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction  

  https://arxiv.org/abs/1811.05660  

8)BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding  

  https://arxiv.org/abs/1810.04805  

9)Attention Is All You Need  

  https://arxiv.org/abs/1706.03762  

10)Single-Shot Refinement Neural Network for Object Detection  

  https://arxiv.org/abs/1711.06897  

11)Automatic chemical design using a data-driven continuous representation of molecules  

  https://arxiv.org/abs/1610.02415