8 accepted papers
Particle accelerators are composed of various components, and their properties are finely tuned to optimize certain particle beam qualities as they accelerate. In particular, particle colliders like the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) are interested in maximiz…
The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the nocloning theorem, resulting in a most desirable computa…
A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties. Ideas of using the quantum internet to address this issue have be…
The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple distributed teachers are trained on disjoin…
Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utiliz…
It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives. Although network models have the potential to be a surrogate for complex human hearing perception, they may contain instabilities in…
This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. The DOTN aims to estimate clean references of noisy speech in a target domain,…
Stochastic neighbor embedding (SNE) aims to transform the observations in high-dimensional space into a low-dimensional space which preserves neighbor identities by minimizing the Kullback-Leibler divergence of the pairwise distributions between two spaces where Gaussian distributions are assumed. D…