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pro vyhledávání: '"Tuckey, David"'
Autor:
Xu, Jie, Saravanan, Karthikeyan, van Dalen, Rogier, Mehmood, Haaris, Tuckey, David, Ozay, Mete
Federated learning (FL) allows clients to collaboratively train a global model without sharing their local data with a server. However, clients' contributions to the server can still leak sensitive information. Differential privacy (DP) addresses suc
Externí odkaz:
http://arxiv.org/abs/2405.06368
Autor:
Zhang, Jisi, Rajan, Vandana, Mehmood, Haaris, Tuckey, David, Parada, Pablo Peso, Jalal, Md Asif, Saravanan, Karthikeyan, Lee, Gil Ho, Lee, Jungin, Jung, Seokyeong
On-device Automatic Speech Recognition (ASR) models trained on speech data of a large population might underperform for individuals unseen during training. This is due to a domain shift between user data and the original training data, differed by us
Externí odkaz:
http://arxiv.org/abs/2401.12085
Publikováno v:
EPTCS 394, 2023, pp. 221-235
In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the se
Externí odkaz:
http://arxiv.org/abs/2205.01514
The Credal semantics is a probabilistic extension of the answer set semantics which can be applied to programs that may or may not be stratified. It assigns to atoms a set of acceptable probability distributions characterised by its lower and upper b
Externí odkaz:
http://arxiv.org/abs/2105.10908
Explainability in AI is gaining attention in the computer science community in response to the increasing success of deep learning and the important need of justifying how such systems make predictions in life-critical applications. The focus of expl
Externí odkaz:
http://arxiv.org/abs/2003.00749
Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications. Our goal is to question the limits of these approaches on more complex tasks. In this paper we apply Layer-Wise R
Externí odkaz:
http://arxiv.org/abs/1907.05664
Akademický článek
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