Zobrazeno 1 - 10
of 3 203
pro vyhledávání: '"A. Elies"'
Autor:
Harington, Elies, Mimram, Samuel
Polynomials in a category have been studied as a generalization of the traditional notion in mathematics. Their construction has recently been extended to higher groupoids, as formalized in homotopy type theory, by Finster, Mimram, Lucas and Seiller,
Externí odkaz:
http://arxiv.org/abs/2411.09950
Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels. While learning-theoretic frameworks establish the sample complexity and the number of measurement shots per sa
Externí odkaz:
http://arxiv.org/abs/2408.05116
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical machine learning. Successful QML models must fulfill the propertie
Externí odkaz:
http://arxiv.org/abs/2406.07072
Autor:
Schott, Lucas, Delas, Josephine, Hajri, Hatem, Gherbi, Elies, Yaich, Reda, Boulahia-Cuppens, Nora, Cuppens, Frederic, Lamprier, Sylvain
Deep Reinforcement Learning (DRL) is an approach for training autonomous agents across various complex environments. Despite its significant performance in well known environments, it remains susceptible to minor conditions variations, raising concer
Externí odkaz:
http://arxiv.org/abs/2403.00420
Publikováno v:
Education + Training, 2024, Vol. 66, Issue 8, pp. 1009-1030.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ET-10-2023-0420
Publikováno v:
Machine Learning: Science and Technology 5, 025003 (2024)
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quan
Externí odkaz:
http://arxiv.org/abs/2309.14419
Autor:
Sweke, Ryan, Recio, Erik, Jerbi, Sofiene, Gil-Fuster, Elies, Fuller, Bryce, Eisert, Jens, Meyer, Johannes Jakob
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as learning model
Externí odkaz:
http://arxiv.org/abs/2309.11647
Autor:
Liu, Qinghui, Fuster-Garcia, Elies, Hovden, Ivar Thokle, Sederevicius, Donatas, Skogen, Karoline, MacIntosh, Bradley J, Grødem, Edvard, Schellhorn, Till, Brandal, Petter, Bjørnerud, Atle, Emblem, Kyrre Eeg
Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The complex interactions between neoplastic cells and normal tissue, as well as the treatment-induced changes often encountered, make glioma tumor growth modeling chal
Externí odkaz:
http://arxiv.org/abs/2309.05406
Publikováno v:
Nature Communications 15, 2277 (2024)
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization fail to
Externí odkaz:
http://arxiv.org/abs/2306.13461
Autor:
Gonzalez, Martin, Fernandez, Nelson, Tran, Thuy, Gherbi, Elies, Hajri, Hatem, Masmoudi, Nader
A potent class of generative models known as Diffusion Probabilistic Models (DPMs) has become prominent. A forward diffusion process adds gradually noise to data, while a model learns to gradually denoise. Sampling from pre-trained DPMs is obtained b
Externí odkaz:
http://arxiv.org/abs/2305.14267