exploRNN: Understanding Recurrent Neural Networks through Visual Exploration

Due to the success of deep learning and its growing job market, students and researchers from many areas are getting interested in learning about deep learning technologies. Visualization has proven to be of great help during this learning process, while most current educational visualizations are targeted towards one specific architecture or use case. Unfortunately, recurrent neural networks (RNNs), which are capable of processing sequential data, are not covered yet, despite the fact that tasks on sequential data, such as text and function analysis, are at the forefront of deep learning research. Therefore, we propose exploRNN, the first interactively explorable, educational visualization for RNNs. exploRNN allows for interactive experimentation with RNNs, and provides in-depth information on their functionality and behavior during training. By defining educational objectives targeted towards understanding RNNs, and using these as guidelines throughout the visual design process, we have designed exploRNN to communicate the most important concepts of RNNs directly within a web browser. By means of exploRNN, we provide an overview of the training process of RNNs at a coarse level, while also allowing detailed inspection of the data-flow within LSTM cells. Within this paper, we motivate our design of exploRNN, detail its realization, and discuss the results of a user study investigating the benefits of exploRNN

exploRNN: teaching recurrent neural networks through visual exploration

Due to the success and growing job market of deep learning (DL), students and researchers from many areas are interested in learning about DL technologies. Visualization has been used as a modern medium during this learning process. However, despite the fact that sequential data tasks, such as text and function analysis, are at the forefront of DL research, there does not yet exist an educational visualization that covers recurrent neural networks (RNNs). Additionally, the benefits and trade-offs between using visualization environments and conventional learning material for DL have not yet been evaluated. To address these gaps, we propose exploRNN, the first interactively explorable educational visualization for RNNs. exploRNNis accessible online and provides an overview of the training process of RNNs at a coarse level, as well as detailed tools for the inspection of data flow within LSTM cells. In an empirical between-subjects study with 37 participants, we investigate the learning outcomes and cognitive load of exploRNN compared to a classic text-based learning environment. While learners in the text group are ahead in superficial knowledge acquisition, exploRNN is particularly helpful for deeper understanding. Additionally, learning with exploRNN is perceived as significantly easier and causes less extraneous load. In conclusion, for difficult learning material, such as neural networks that require deep understanding, interactive visualizations such as exploRNN can be helpful

Incidence of cardiovascular events in patients with stabilized coronary heart disease: the EUROASPIRE IV follow-up study

The EUROASPIRE surveys (EUROpean Action on Secondary Prevention through Intervention to Reduce Events) demonstrated that most European coronary patients fail to achieve lifestyle, risk factor and therapeutic targets. Here we report on the 2-year incidence of hard cardiovascular (CV) endpoints in the EUROASPIRE IV cohort. EUROASPIRE IV (2012-2013) was a large cross-sectional study undertaken at 78 centres from selected geographical areas in 24 European countries. Patients were interviewed and examined at least 6months following hospitalization for a coronary event or procedure. Fatal and non-fatal CV events occurring at least 1year after this baseline screening were registered. The primary outcome in our analyses was the incidence of CV death or non-fatal myocardial infarction, stroke or heart failure. Cox regression models, stratified for country, were fitted to relate baseline characteristics to outcome. Our analyses included 7471 predominantly male patients. Overall, 222 deaths were registered of whom 58% were cardiovascular. The incidence of the primary outcome was 42 per 1000 person-years. Comorbidities were strongly and significantly associated with the primary outcome (multivariately adjusted hazard ratio HR, 95% confidence interval): severe chronic kidney disease (HR 2.36, 1.44-3.85), uncontrolled diabetes (HR 1.89, 1.50-2.38), resting heart rate 75bpm (HR 1.74, 1.30-2.32), history of stroke (HR 1.70, 1.27-2.29), peripheral artery disease (HR 1.48, 1.09-2.01), history of heart failure (HR 1.47, 1.08-2.01) and history of acute myocardial infarction (HR 1.27, 1.05-1.53). Low education and feelings of depression were significantly associated with increased risk. Lifestyle factors such as persistent smoking, insufficient physical activity and central obesity were not significantly related to adverse outcome. Blood pressure and LDL-C levels appeared to be unrelated to cardiovascular events irrespective of treatment. In patients with stabilized CHD, comorbid conditions that may reflect the ubiquitous nature of atherosclerosis, dominate lifestyle-related and other modifiable risk factors in terms of prognosis, at least over a 2-year follow-up perio