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.
Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science
- Sprint planning – I’m going to be busy
- More work with Rukan. We’re going to focus on some simple spikes
- The simple spikes look great. We’re going to do a sensitivity analysis on the MDS data now
- Got my fancy query working
create or replace view view_combined as
select distinct e.id, e.name, e.description, s1.value as dimension_size, s2.value as layers,
r1.value as avg_cos_loss, r2.value as avg_l1_loss from
join table_settings s1 on e.id = s1.experiment_id and s1.name = 'dimension_size'
join table_settings s2 on e.id = s2.experiment_id and s2.name = 'layers'
join table_results r1 on e.id = r1.experiment_id and r1.name = 'avg cosine loss'
join table_results r2 on e.id = r2.experiment_id and r2.name = 'avg l1 loss';
select * from view_combined where id = 100;
- Parsing Yelp
- Pew Center: Search Results For: covid
- 3:00 Meeting