Zobrazeno 1 - 10
of 6 630
pro vyhledávání: '"Devos P"'
Autor:
Capponi, Sylvain, Devos, Lukas, Lecheminant, Philippe, Totsuka, Keisuke, Vanderstraeten, Laurens
We investigate the nature of the quantum phase transition in modulated SU(N) Heisenberg spin chains. In the odd-N case, the transition separates a trivial non-degenerate phase to a doubly-degenerate gapped chiral PSU(N) symmetry-protected topological
Externí odkaz:
http://arxiv.org/abs/2409.01019
IT Governance systems are increasingly required to keep todays organizations functioning. IT Governance requires a holistic system of interacting components, including processes, organizational structures, information, and others. Performance managem
Externí odkaz:
http://arxiv.org/abs/2405.04558
Autor:
Ghaffari, Houtan, Devos, Paul
Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning, particularly in data-scarce scenarios. Pre-training refers to the initial step of training models outside the current task of in
Externí odkaz:
http://arxiv.org/abs/2404.17252
Autor:
Mortier, Quinten, Devos, Lukas, Burgelman, Lander, Vanhecke, Bram, Bultinck, Nick, Verstraete, Frank, Haegeman, Jutho, Vanderstraeten, Laurens
We show how fermionic statistics can be naturally incorporated in tensor networks on arbitrary graphs through the use of graded Hilbert spaces. This formalism allows to use tensor network methods for fermionic lattice systems in a local way, avoiding
Externí odkaz:
http://arxiv.org/abs/2404.14611
Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advanc
Externí odkaz:
http://arxiv.org/abs/2403.15489
Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, i.e., slightly perturbed examples that elicit a misprediction. There has been significant research on designing approaches to
Externí odkaz:
http://arxiv.org/abs/2402.08586
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems,2024
Recurrent Neural Networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applications. Nevertheless, vanilla RNNs are confronted with the well-known iss
Externí odkaz:
http://arxiv.org/abs/2310.14982
Graph representations are the generalization of geometric graph drawings from the plane to higher dimensions. A method introduced by Tutte to optimize properties of graph drawings is to minimize their energy. We explore this minimization for spherica
Externí odkaz:
http://arxiv.org/abs/2309.02817
In 1983, Bouchet proved that every bidirected graph with a nowhere-zero integer-flow has a nowhere-zero 216-flow, and conjectured that 216 could be replaced with 6. This paper shows that for cyclically 5-edge-connected bidirected graphs that number c
Externí odkaz:
http://arxiv.org/abs/2309.00704
Publikováno v:
Remote Sensing in Ecology and Conservation, Vol 10, Iss 5, Pp 642-654 (2024)
Abstract Passive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requir
Externí odkaz:
https://doaj.org/article/5e8fd760d907448385191759efa805c2
