Summer 2022
Paper Plan
(1) DeepCloud: CloudGAN for cloud detection
(2) DeepAOD: AODGAN for aerosol optical depth prediction
(3) DeepIOP: IOPGAN for IOP prediction
(4) DL-MSA: Actor-Critic Methods
(5) DL-PSSP: CNN-LSTM Models
Paper Reading List
I. Theory of Deep Learning
Convolutional Neural Networks (CNNs)
- Deep learning. Y LeCun, Y Bengio, G Hinton – nature, 2015 – nature.com . [https://www.nature.com/articles/nature14539]
- ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012. [https://dl.acm.org/citation.cfm?id=3065386]
- Going deeper with convolutions. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [https://ieeexplore.ieee.org/document/7298594]
Recurrent Neural Networks (RNNs)
- A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Y Yu, X Si, C Hu, J Zhang – Neural computation, 2019 – MIT Press. [https://www.mitpressjournals.org/doi/full/10.1162/neco_a_01199]
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio. 2015. [https://arxiv.org/abs/1502.03044]
Reinforcement Learning
- Deep Reinforcement Learning: An Overview. Yuxi Li. [Preprint: https://arxiv.org/abs/1701.07274]
- Key Papers in Deep Reinforcement Learning. [https://spinningup.openai.com/en/latest/spinningup/keypapers.html]
II. Application of Deep Learning
Deep Learning for Earth Science
- TBA
Biomedical Image Analysis
- Overview of Deep Learning in Gastrointestinal Endoscopy. Jun Ki Min, Min Seob Kwak, and Jae Myung Cha. Gut Liver. 2019 Jul; 13(4): 388–393. [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6622562/ ]
- Kvasir: A Multi-Class Image Dataset for Computer Aided
Gastrointestinal Disease Detection. Pogorelov et al. ACM Multimedia System, 2017. [https://www.researchgate.net/publication/316215961_KVASIR_A_Multi-Class_Image_Dataset_for_Computer_Aided_Gastrointestinal_Disease_Detection] - Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images. Shichijo S et al. EBioMedicine. 2017 Nov;25:106-111. [https://www.sciencedirect.com/science/article/pii/S2352396417304127?via%3Dihub]
Protein Function Prediction
- A Reinforcement Learning Based Approach to Multiple Sequence Alignment. Ioan-Gabriel Mircea, Iuliana Bocicor, Gabriela Czibula. In: Balas V., Jain L., Balas M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 634. [https://link.springer.com/chapter/10.1007/978-3-319-62524-9_6]
- Using deep reinforcement learning approach for solving the multiple sequence alignment problem. Jafari, R., Javidi, M. & Kuchaki Rafsanjani, M. SN Appl. Sci. (2019) 1: 592. [https://doi.org/10.1007/s42452-019-0611-4]
- A Critical Review of Recurrent Neural Networks for Sequence Learning. Zachary C. Lipton, John Berkowitz, Charles Elkan. [Preprint: https://arxiv.org/abs/1506.00019 ]
- DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks. Ahmet Sureyya Rifaioglu, Tunca Doğan, Maria Jesus Martin, Rengul Cetin-Atalay & Volkan Atalay. Scientific Reports. volume 9, Article number: 7344 (2019). [https://www.nature.com/articles/s41598-019-43708-3]
- Deep Recurrent Neural Network for Protein Function Prediction from Sequence. Xueliang Leon Liu. [Pre-print] [https://arxiv.org/ftp/arxiv/papers/1701/1701.08318.pdf]
- DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Kulmanov et al. Bioinformatics, Volume 34, Issue 4, 15 February 2018, Pages 660–668. [https://academic.oup.com/bioinformatics/article/34/4/660/4265461 ]
- Hierarchical Multi-Label Classification Networks. Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros. CML 2018. [https://icml.cc/Conferences/2018/Schedule?showEvent=2306 ]
- Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020). https://doi.org/10.1038/s41586-019-1923-7
Protein Secondary Structure Prediction
- Papers to read: Link.
Neuro-inspired DL Systems and Algorithms
- Theory of cortical function. David J. Heeger. PNAS, 2017 114 (8) 1773-1782. [https://doi.org/10.1073/pnas.1619788114]
- Reducing the Dimensionality of Data with Neural Networks. G. E. Hinton* and R. R. Salakhutdinov, Science, 313, 2006. [https://www.cs.toronto.edu/~hinton/science.pdf]
- Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B. Issa &
James J. DiCarlo. Neuroscience 22, 974–983 (2019). [https://www.nature.com/articles/s41593-019-0392-5]
- Towards a Mathematical Theory of Cortical Micro-circuits. Dileep George , Jeff Hawkins. PLoS Comput Biol 5(10): e1000532, 2019. [https://doi.org/10.1371/journal.pcbi.1000532]
III. Implementation of Deep Learning
- TensorFlow: A system for large-scale machine learning. Martin Abadi, et al. 2th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX Association (2016), pp. 265-283. [ https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf ]
- Seide, Frank & Agarwal, Amit. (2016). CNTK: Microsoft’s Open-Source Deep-Learning Toolkit. Proc. KDD ’16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 2135-2135. [ https://dl.acm.org/citation.cfm?id=2945397]
- “Deep Learning Explained, ” A free course on CNTK by Microsoft: https://www.edx.org/course/deep-learning-explained-5.
- Chollet, F. (2015) Keras, GitHub. https://github.com/fchollet/keras.
Top Conferences for Machine Learning & Artificial Intelligence
More papers to read (Link)