Zobrazeno 1 - 10
of 250 571
pro vyhledávání: '"Petersen AS"'
Autor:
Gentile, Marziogiuseppe, Gerlach, Marius, Richter, Robert, van Setten, Michiel J., Petersen, John S., van der Heide, Paul, Holzmeier, Fabian
Publikováno v:
Proc. SPIE 12498, Advances in Patterning Materials and Processes XL, 124980S (30 April 2023)
The dissociative photoionization of \textit{tert}-butyl methyl methacrylate, a monomer unit found in many ESCAP resists, was investigated in a gas phase photoelectron photoion coincidence experiment employing extreme ultraviolet (EUV) synchrotron rad
Externí odkaz:
http://arxiv.org/abs/2411.10177
Autor:
Khare, Smith K., Booth, Berit Bargum, Blanes-Vidal, Victoria, Petersen, Lone Kjeld, Nadimi, Esmaeil S.
Cervical cancer remains a major worldwide health issue, with early identification and risk assessment playing critical roles in effective preventive interventions. This paper presents the Cervix-AID-Net model for cervical precancer risk classificatio
Externí odkaz:
http://arxiv.org/abs/2411.09469
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hier
Externí odkaz:
http://arxiv.org/abs/2411.09864
In this paper, an $\mathscr{H}_2$ norm-based model reduction method for linear quantum systems is presented, which can obtain a physically realizable model with a reduced order for closely approximating the original system. The model reduction proble
Externí odkaz:
http://arxiv.org/abs/2411.07603
In recent work it has been shown that determining a feedforward ReLU neural network to within high uniform accuracy from point samples suffers from the curse of dimensionality in terms of the number of samples needed. As a consequence, feedforward Re
Externí odkaz:
http://arxiv.org/abs/2411.05453
With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference. Recently, an approach for learning logic gate networks directly via a differentiable relaxation was proposed. Logi
Externí odkaz:
http://arxiv.org/abs/2411.04732
In this paper, we develop an online optimization algorithm for solving a class of nonconvex optimization problems with a linearly varying optimal point. The global convergence of the algorithm is guaranteed using the circle criterion for the class of
Externí odkaz:
http://arxiv.org/abs/2411.01826
We consider the training of the first layer of vision models and notice the clear relationship between pixel values and gradient update magnitudes: the gradients arriving at the weights of a first layer are by definition directly proportional to (nor
Externí odkaz:
http://arxiv.org/abs/2410.23970
Autor:
Mylonas, Georgios, Kalogeras, Athanasios, Petersen, Sobah Abbas, Muñoz, Luis, Chatzigiannakis, Ioannis
Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerou
Externí odkaz:
http://arxiv.org/abs/2410.22012
Autor:
Petersen, Felix, Borgelt, Christian, Sutter, Tobias, Kuehne, Hilde, Deussen, Oliver, Ermon, Stefano
When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular approach is to continuously relax the objectives to provide gradients, ena
Externí odkaz:
http://arxiv.org/abs/2410.19055