254 citations · 397 across the 6 of their papers we have counts for
7 papers
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces
Bert Moons, Parham Noorzad, Andrii Skliar +4
Current state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to multiple hardware platforms, nor handle diverse architectural search-spaces. To remed…
Mixed-precision deep learning based on computational memory
S. R. Nandakumar, Manuel Le Gallo, Christophe Piveteau +11
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition…
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…
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
Florian Scheidegger, Roxana Istrate, Giovanni Mariani +3
In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, a…
BAGAN: Data Augmentation with Balancing GAN
Giovanni Mariani, Florian Scheidegger, Roxana Istrate +2
Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN)…