254 citations · 322 across the 9 of their papers we have counts for
9 papers
Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti +5
Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost -- tens of thousands of core-hours per high-fidelity evaluation -- severely limits t…
Counterfactual Image Generation for adversarially robust and interpretable Classifiers
Rafael Bischof, Florian Scheidegger, Michael A. Kraus +1
Neural Image Classifiers are effective but inherently hard to interpret and susceptible to adversarial attacks. Solutions to both problems exist, among others, in the form of count…
Constrained deep neural network architecture search for IoT devices accounting hardware calibration
Florian Scheidegger, Luca Benini, Costas Bekas +1
Deep neural networks achieve outstanding results in challenging image classification tasks. However, the design of network topologies is a complex task and the research community m…
PAGAN: Portfolio Analysis with Generative Adversarial Networks
Giovanni Mariani, Yada Zhu, Jianbo Li +4
Since decades, the data science community tries to propose prediction models of financial time series. Yet, driven by the rapid development of information technology and machine in…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…
TAPAS: Train-less Accuracy Predictor for Architecture Search
R. Istrate, F. Scheidegger, G. Mariani +3
In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinfo…