References of "Varisteas, Georgios 50023637"
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See detailTime Series Modeling of Market Price in Real-Time Bidding
Du, Manxing UL; Hammerschmidt, Christian UL; Varisteas, Georgios UL et al

in 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2019, April)

Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions ... [more ▼]

Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions fluctuate heavily within short time spans. State-of-the-art methods neglect the temporal dependencies of bidders’ behaviors. In this paper, the bid requests are aggregated by time and the mean market price per aggregated segment is modeled as a time series. We show that the Long Short Term Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies in the market price. We further improve the predicting performance by adding a summary of exogenous features from bid requests. [less ▲]

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See detailEvaluation of End-To-End Learning for Autonomous Driving: The Good, the Bad and the Ugly
Varisteas, Georgios UL; Frank, Raphaël UL; Sajadi Alamdari, Seyed Amin UL et al

in 2nd International Conference on Intelligent Autonomous Systems, Singapore, Feb. 28 to Mar. 2, 2019 (2019, March 01)

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See detailVisualizing the Learning Progress of Self-Driving Cars
Mund, Sandro; Frank, Raphaël UL; Varisteas, Georgios UL et al

in 21st International Conference on Intelligent Transportation Systems (2018, November 02)

Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models ... [more ▼]

Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models perform so well. In order for them to become a commercially viable solution, it first needs to be understood why a certain behavior is triggered and how and what those networks learn from human-generated driving data to ensure safety. One research direction is to visualize what the network sees by highlighting regions of an image that influence the outcome of the model. In this vein, we propose a generic visualization method using Attention Heatmaps (AHs) to highlight what a given Convolutional Neural Network (CNN) learns over time. To do so, we rely on a novel occlusion technique to mask different regions of an input image to observe the effect on a predicted steering signal. We then gradually increase the amount of training data and study the effect on the resulting Attention Heatmaps, both in terms of visual focus and temporal behavior. [less ▲]

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See detailDetecting Malicious Authentication Events Trustfully
Kaiafas, Georgios UL; Varisteas, Georgios UL; Lagraa, Sofiane UL et al

in Kaiafas, Georgios; Varisteas, Georgios; Lagraa, Sofiane (Eds.) et al IEEE/IFIP Network Operations and Management Symposium, 23-27 April 2018, Taipei, Taiwan Cognitive Management in a Cyber World (2018)

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See detailDistributed C++-Python embedding for fast predictions and fast prototyping
Varisteas, Georgios UL; Avanesov, Tigran UL; State, Radu UL

in Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning (2018)

Python has evolved to become the most popular language for data science. It sports state-of-the-art libraries for analytics and machine learning, like Sci-Kit Learn. However, Python lacks the ... [more ▼]

Python has evolved to become the most popular language for data science. It sports state-of-the-art libraries for analytics and machine learning, like Sci-Kit Learn. However, Python lacks the computational performance that a industrial system requires for high frequency real time predictions. Building upon a year long research project heavily based on SciKit Learn (sklearn), we faced performance issues in deploying to production. Replacing sklearn with a better performing framework would require re-evaluating and tuning hyperparameters from scratch. Instead we developed a python embedding in a C++ based server application that increased performance by up to 20x, achieving linear scalability up to a point of convergence. Our implementation was done for mainstream cost effective hardware, which means we observed similar performance gains on small as well as large systems, from a laptop to an Amazon EC2 instance to a high-end server. [less ▲]

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See detailImproving Real-Time Bidding Using a Constrained Markov Decision Process
Du, Manxing UL; Sassioui, Redouane UL; Varisteas, Georgios UL et al

in Proceedings of the 13th International Conference on Advanced Data Mining and Applications (2017, November)

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See detailPandia: comprehensive contention-sensitive thread placement
Goodman, Daniel; Varisteas, Georgios UL; Harris, Tim

in Proceedings of the Twelfth European Conference on Computer Systems (2017)

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