Deep Learning Applications for COVID-19
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Abstract: This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research. Authors: Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht (Florida Atlantic University) Paper Links Mentioned in the Video: Mapping the Landscape of Artificial Intelligence Applications against COVID-19: https://arxiv.org/abs/2003.11336 Leveraging Data Science to Combat COVID-19: A Comprehensive Review: https://www.techrxiv.org/articles/preprint/Leveraging_Data_Science_To_Combat_COVID-19_A_Comprehensive_Review/12212516 AlphaFold2: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology Molecular Representation Learning with Language Models and Auxiliary Tasks: https://arxiv.org/abs/2011.13230 Data Professor - Machine Learning for Drug Discovery explained in 2 minutes: https://www.youtube.com/watch?v=xDMzOUUnNzw Understanding LSTM Networks - Christopher Olah: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ Vision Transformer: https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html Thomas Kipf - Graph Convolutional Networks: https://tkipf.github.io/graph-convolutional-networks/ Attention is all you Need: https://arxiv.org/abs/1706.03762 GLUE Benchmark: https://gluebenchmark.com/ SQuAD Dataset Page: https://rajpurkar.github.io/SQuAD-explorer/ TL;DR Nature Article: https://www.nature.com/articles/d41586-020-03277-2 TREC-COVID: https://arxiv.org/abs/2005.04474 CO-Search: https://arxiv.org/abs/2006.09595 BenevolentAI: https://www.benevolent.com/ SciFACT Demo App: https://scifact.apps.allenai.org/ FAIR / NYU COVID-19 CXR Deterioration Modeling: https://ai.facebook.com/blog/new-ai-research-to-help-predict-covid-19-resource-needs-from-a-series-of-x-rays/ MoCo pretraining in CXR: https://arxiv.org/abs/2010.05352 OpenAI CLIP: https://openai.com/blog/clip/ Contrastive Learning of Medical Visual Representations from Paired Images and Text: https://arxiv.org/abs/2010.00747 Illuminating the dark spaces of healthcare with Ambient Intelligence: https://www.nature.com/articles/s41586-020-2669-y CovariantAI: https://covariant.ai/ Solving Rubik's Cube with a Robot Hand: https://openai.com/blog/solving-rubiks-cube/ Berkeley AI Research Blog: https://bair.berkeley.edu/blog/ Four Novel Approaches to Manipulate Fabric: https://bair.berkeley.edu/blog/2020/05/05/fabrics/ Ingredients of Real-World Robotic Learning: https://bair.berkeley.edu/blog/2020/04/27/ingredients/ RoboNet: https://bair.berkeley.edu/blog/2019/11/26/robo-net/ Plan2Explore: https://bair.berkeley.edu/blog/2020/10/06/plan2explore/ An Empirical Study of Representation Learning for RL in Healthcare: https://arxiv.org/abs/2011.11235 AlphaFold for COVID Proteins: https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19 Artificial Intelligence in COVID-19 drug repurposing: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30192-8/fulltext Worldometeres COVID Cases: https://www.worldometers.info/coronavirus/ Hi-COVIDNet: https://dl.acm.org/doi/10.1145/3394486.3412864 LIME: https://arxiv.org/abs/1602.04938 DeepDream: https://en.wikipedia.org/wiki/DeepDream WT5?! https://arxiv.org/abs/2004.14546 CheckList: https://arxiv.org/abs/2005.04118 DALL-E: https://openai.com/blog/dall-e/ On the steerability of GANs: https://arxiv.org/abs/1907.07171 Federated Learning: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html Andrew Trask on Privacy Preserving AI: https://www.youtube.com/watch?v=4zrU54VIK6k&t=870s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547

0:00 Intro 1:05 Video Outline 2:11 Application Preview 6:55 Limitations Preview 8:00 Data Science for COVID 9:30 Learning Tasks 18:00 Input Data for Deep Learning 34:06 Natural Language Processing 54:43 Computer Vision 1:07:35 Life Sciences 1:18:18 Epidemiology 1:22:28 Limitations of Deep Learning 1:36:23 On the Measure of Intelligence 1:37:14 Takeaways and Lessons Learned
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