Results 21 to 30 of about 236,656 (279)

Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks

Nature Communications, 2022
Designing efficient Bayesian neural networks remains a challenge. Here, the authors use the cycle variation in the programming of the 2D memtransistors to achieve Gaussian random number generator-based synapses, and combine it with the complementary 2D ...
Amritanand Sebastian, Rahul Pendurthi, Azimkhan Kozhakhmetov, Nicholas Trainor, Joshua A. Robinson, Joan M. Redwing, Saptarshi Das   +6 more
doaj   +1 more source

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors [PDF]

, 2010
The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties.
A Bodek, A Bodek, AA Aguilar-Arevalo, AF Krutov, AF Sill, AV Afanasev, B Denby, B Mellado, C Berger, CE Carlson, CF Perdrisat, CH Llewellyn Smith, CM Bishop, D Marquardt, DH Beck, DJC MacKay, DJC MacKay, DJC MacKay, E Geis, EJ Brash, EL Lomon, EL Lomon, F Borkowski, F Cardarelli, G Hohler, GA Miller, GG Simon, IA Qattan, J Arrington, J Arrington, J Arrington, J Damgov, J Liu, JJ Kelly, JJ Murphy, K Hornik, K Kurek, K Levenberg, KM Hanson, Krzysztof M. Graczyk, KS Kim, KS Kuzmin, L Andivahis, L Debbio Del, L Debbio Del, L Álvarez-Ruso, LE Price, M Leshno, MA Belushkin, MH Ahn, MM Giannini, O Gayou, PAM Guichon, PE Bosted, PE Bosted, PG Blunden, Piotr Płonski, PM Williams, R Gran, RC Walker, RD Ball, RD Ball, RD Ball, RD Young, RF Wagenbrunn, Robert Sulej, S Forte, S Galster, S Geman, SF Gull, SF Gull, V Bernard, V Kurková, W Bartel, W Xu, WM Alberico, WM Alberico, WM Alberico, YC Chen   +78 more
core   +1 more source

Quantum Bayesian Neural Networks

, 2021
17 pages, 11 ...
Berner, Noah, Fortuin, Vincent, Landman, Jonas   +2 more
openaire   +2 more sources

Bayesian Quantum Neural Networks

IEEE Access, 2022
The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine Learning or Quantum Machine Intelligence.
Nam Nguyen, Kwang-Cheng Chen
openaire   +2 more sources

Explaining Bayesian Neural Networks

, 2021
To advance the transparency of learning machines such as Deep Neural Networks (DNNs), the field of Explainable AI (XAI) was established to provide interpretations of DNNs' predictions. While different explanation techniques exist, a popular approach is given in the form of attribution maps, which illustrate, given a particular data point, the relevant ...
Bykov, Kirill, Höhne, Marina M. -C., Creosteanu, Adelaida, Müller, Klaus-Robert, Klauschen, Frederick, Nakajima, Shinichi, Kloft, Marius   +6 more
openaire   +2 more sources

Ship Target Identification via Bayesian-Transformer Neural Network

Journal of Marine Science and Engineering, 2022
Ship target identification is of great significance in both military and civilian fields. Many methods have been proposed to identify the targets using tracks information. However, most of existing studies can only identify two or three types of targets,
Zhan Kong, Yaqi Cui, Wei Xiong, Fucheng Yang, Zhenyu Xiong, Pingliang Xu   +5 more
doaj   +1 more source

Predictions of bitcoin prices through machine learning based frameworks [PDF]

PeerJ Computer Science, 2021
The high volatility of an asset in financial markets is commonly seen as a negative factor. However short-term trades may entail high profits if traders open and close the correct positions.
Luisanna Cocco, Roberto Tonelli, Michele Marchesi   +2 more
doaj   +2 more sources

Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

PRX Quantum, 2021
Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time, or nonclassical quantum states.
Lukas J. Fiderer, Jonas Schuff, Daniel Braun   +2 more
doaj   +1 more source

Simple Direct Uncertainty Quantification Technique Based on Machine Learning Regression

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Epistemic uncertainty quantification provides useful insight into both deep and shallow neural networks' understanding of the relationships between their training distributions and unseen instances and can serve as an estimate of classification ...
Katherine E. Brown, Douglas A. Talbert
doaj   +1 more source

Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference

Sensors, 2022
In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years.
Monika E. Heringhaus, Yi Zhang, André Zimmermann, Lars Mikelsons   +3 more
doaj   +1 more source