Zobrazeno 1 - 10
of 21 088
pro vyhledávání: '"A. Pezeshki"'
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations.
Externí odkaz:
http://arxiv.org/abs/2412.07684
Entangled matter displays unusual and attractive properties and mechanisms: tensile strength, capabilities for assembly and disassembly, damage tolerance. While some of the attributes and mechanisms share some traits with traditional granular materia
Externí odkaz:
http://arxiv.org/abs/2412.05415
Entangled matter provides intriguing perspectives in terms of deformation mechanisms, mechanical properties, assembly and disassembly. However, collective entanglement mechanisms are complex, occur over multiple length scales, and they are not fully
Externí odkaz:
http://arxiv.org/abs/2412.05416
Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces. Thus, it is essential to leverage existing data to avoid cost
Externí odkaz:
http://arxiv.org/abs/2412.02089
In this work, we tackle a challenging and extreme form of subpopulation shift, which is termed compositional shift. Under compositional shifts, some combinations of attributes are totally absent from the training distribution but present in the test
Externí odkaz:
http://arxiv.org/abs/2410.06303
In this work, we introduce a new approach to processing complex-valued data using DNNs consisting of parallel real-valued subnetworks with coupled outputs. Our proposed class of architectures, referred to as Steinmetz Neural Networks, leverages multi
Externí odkaz:
http://arxiv.org/abs/2409.10075
We present a simple performance bound for the greedy scheme in string optimization problems that obtains strong results. Our approach vastly generalizes the group of previously established greedy curvature bounds by Conforti and Cornu\'{e}jols (1984)
Externí odkaz:
http://arxiv.org/abs/2409.05020
Autor:
Li, Pingzhi, Kools, Thomas J., Pezeshki, Hamed, Joosten, Joao M. B. E., Li, Jianing, Igarashi, Junta, Hohlfeld, Julius, Lavrijsen, Reinoud, Mangin, Stephane, Malinowski, Gregory, Koopmans, Bert
Single pulse all-optical switching of magnetization (AOS) in Co/Gd based synthetic ferrimagnets carries promises for hybrid spintronic-photonic integration. A crucial next step progressing towards this vision is to gain insight into AOS and multi-dom
Externí odkaz:
http://arxiv.org/abs/2406.16027
Autor:
Venkatasubramanian, Shyam, Kang, Bosung, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
This work presents a large-scale dataset for radar adaptive signal processing (RASP) applications, aimed at supporting the development of data-driven models within the radar community. The dataset, called RASPNet, consists of 100 realistic scenarios
Externí odkaz:
http://arxiv.org/abs/2406.09638
Autor:
Jin, Xin, Aglieri, Vincenzo, Jeong, Young-Gyun, Pezeshki, Atiye, Skokan, Lilian, Shagar, Mostafa, Jia, Yuechen, Bianucci, Pablo, Ruediger, Andreas, Orgiu, Emanuele, Toma, Andrea, Razzari, Luca
Two-dimensional materials, including transition metal dichalcogenides, are attractive for a variety of applications in electronics as well as photonics and have recently been envisioned as an appealing platform for phonon polaritonics. However, their
Externí odkaz:
http://arxiv.org/abs/2405.08851