Zobrazeno 1 - 10
of 4 344
pro vyhledávání: '"Clerico A"'
Autor:
Salvador-Sánchez, J., Pérez-Rodriguez, A., Clericò, V., Zheliuk, O., Zeitler, U., Watanabe, K., Taniguchi, T., Diez, E., Amado, M., Bellani, V.
Publikováno v:
Eur. Phys. J. Plus 139, 979 (2024)
In a twisted graphene on hexagonal Boron Nitride, the presence of a gap and the breaking of the symmetry between carbon sublattices leads to multicomponent fractional quantum Hall effect (FQHE) due to the electrons correlation. We report on the FQHE
Externí odkaz:
http://arxiv.org/abs/2411.07958
Traditional generalization results in statistical learning require a training data set made of independently drawn examples. Most of the recent efforts to relax this independence assumption have considered either purely temporal (mixing) dependencies
Externí odkaz:
http://arxiv.org/abs/2410.08977
Autor:
Clerico, Victoria, Snyder, Shay, Lohia, Arya, Kaiser, Md Abdullah-Al, Schwartz, Gregory, Jaiswal, Akhilesh, Parsa, Maryam
Dynamic Vision Sensors (DVS) have emerged as a revolutionary technology with a high temporal resolution that far surpasses RGB cameras. DVS technology draws biological inspiration from photoreceptors and the initial retinal synapse. Our research show
Externí odkaz:
http://arxiv.org/abs/2408.09454
Autor:
Sinaga, Jason, Clerico, Victoria, Kaiser, Md Abdullah-Al, Snyder, Shay, Lohia, Arya, Schwartz, Gregory, Parsa, Maryam, Jaiswal, Akhilesh
Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Mo
Externí odkaz:
http://arxiv.org/abs/2408.08320
Autor:
Estrada-Álvarez, Jorge, Salvador-Sánchez, Juan, Pérez-Rodríguez, Ana, Sánchez-Sánchez, Carlos, Clericò, Vito, Vaquero, Daniel, Watanabe, Kenji, Taniguchi, Takashi, Diez, Enrique, Domínguez-Adame, Francisco, Amado, Mario, Díaz, Elena
Viscous electron flow exhibits exotic signatures such as superballistic conduction. Bending the geometry of the device is a must to observe hydrodynamic effects. To this end, we build three antidot graphene superlattices with different hole diameters
Externí odkaz:
http://arxiv.org/abs/2407.04527
We study the generalization error of statistical learning algorithms in a non-i.i.d. setting, where the training data is sampled from a stationary mixing process. We develop an analytic framework for this scenario based on a reduction to online learn
Externí odkaz:
http://arxiv.org/abs/2406.12600
In parallel with the continuously increasing parameter space dimensionality, search and optimization algorithms should support distributed parameter evaluations to reduce cumulative runtime. Intel's neuromorphic optimization library, Lava-Optimizatio
Externí odkaz:
http://arxiv.org/abs/2405.04387
Autor:
Snyder, Shay, Clerico, Victoria, Cong, Guojing, Kulkarni, Shruti, Schuman, Catherine, Risbud, Sumedh R., Parsa, Maryam
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within
Externí odkaz:
http://arxiv.org/abs/2404.17048
Autor:
Clerico, Eugenio, Guedj, Benjamin
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value. This keeps the network in a lazy training regime, where the dynamics can be lin
Externí odkaz:
http://arxiv.org/abs/2312.13259
Although radar and communications signal classification are usually treated separately, they share similar characteristics, and methods applied in one domain can be potentially applied in the other. We propose a simple and unified scheme for the clas
Externí odkaz:
http://arxiv.org/abs/2305.03192