29 citations · 48 across the 7 of their papers we have counts for
9 papers
Boosting Method for Automated Feature Space Discovery in Supervised Quantum Machine Learning Models
Vladimir Rastunkov, Jae-Eun Park, Abhijit Mitra +5
Quantum Support Vector Machines (QSVM) have become an important tool in research and applications of quantum kernel methods. In this work we propose a boosting approach for buildin…
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been employed widely in computational creativity research. However,…
Predicting Deep Neural Network Generalization with Perturbation Response Curves
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of prediction tasks. However, despite these successes, the recent Predicting Gener…
Towards creativity characterization of generative models via group-based subset scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution…
Gi and Pal Scores: Deep Neural Network Generalization Statistics
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks. However, despite these successes…
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting
Nam Nguyen, Brian Quanz
Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most prev…