Zobrazeno 1 - 10
of 19 587
pro vyhledávání: '"Işik A"'
Autor:
Arda Bayar, Tarik Ercan, Cem Şener, Asu Fergün Yilmaz, Tayfur Toptaş, Işik Atagündüz, Ayşe Tülin Tuğlular
Publikováno v:
HemaSphere, Vol 7, p e60378b8 (2023)
Externí odkaz:
https://doaj.org/article/bcd2b6567bf548a8a2f331d6df2a59d3
This paper introduces a novel approach in neuromorphic computing, integrating heterogeneous hardware nodes into a unified, massively parallel architecture. Our system transcends traditional single-node constraints, harnessing the neural structure and
Externí odkaz:
http://arxiv.org/abs/2410.00295
Autor:
Isik, Erman, Lei, Antonio
Let $E$ be an elliptic curve defined over $\mathbb{Q}$, and let $K$ be an imaginary quadratic field. Consider an odd prime $p$ at which $E$ has good supersingular reduction with $a_p(E)=0$ and which is inert in $K$. Under the assumption that the sign
Externí odkaz:
http://arxiv.org/abs/2409.02202
In our study, we utilized Intel's Loihi-2 neuromorphic chip to enhance sensor fusion in fields like robotics and autonomous systems, focusing on datasets such as AIODrive, Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19
Externí odkaz:
http://arxiv.org/abs/2408.16096
Understanding people's social interactions in complex real-world scenarios often relies on intricate mental reasoning. To truly understand how and why people interact with one another, we must infer the underlying mental states that give rise to the
Externí odkaz:
http://arxiv.org/abs/2408.12574
Autor:
Abdioglu, Hasan Berkay, Isik, Yagmur, Sevgi, Merve, Kirabali, Ufuk Gorkem, Mert, Yunus Emre, Guldogan, Gulnihal, Serdarli, Selin, Gulen, Tarik Taha, Uvet, Huseyin
Accurately measuring cell stiffness is challenging due to the invasiveness of traditional methods like atomic force microscopy (AFM) and optical stretching. We introduce a non-invasive off-axis system using holographic imaging and acoustic stimulatio
Externí odkaz:
http://arxiv.org/abs/2407.21182
This study investigates the realm of liquid neural networks (LNNs) and their deployment on neuromorphic hardware platforms. It provides an in-depth analysis of Liquid State Machines (LSMs) and explores the adaptation of LNN architectures to neuromorp
Externí odkaz:
http://arxiv.org/abs/2407.20590
Autor:
Panda, Ashwinee, Isik, Berivan, Qi, Xiangyu, Koyejo, Sanmi, Weissman, Tsachy, Mittal, Prateek
Existing methods for adapting large language models (LLMs) to new tasks are not suited to multi-task adaptation because they modify all the model weights -- causing destructive interference between tasks. The resulting effects, such as catastrophic f
Externí odkaz:
http://arxiv.org/abs/2406.16797
Conventional relays face challenges for transmission lines connected to inverter-based resources (IBRs). In this article, a single-ended intelligent protection of the transmission line in the zone between the grid and the PV farm is suggested. The me
Externí odkaz:
http://arxiv.org/abs/2406.13194
Autor:
Schaeffer, Rylan, Lecomte, Victor, Pai, Dhruv Bhandarkar, Carranza, Andres, Isik, Berivan, Unell, Alyssa, Khona, Mikail, Yerxa, Thomas, LeCun, Yann, Chung, SueYeon, Gromov, Andrey, Shwartz-Ziv, Ravid, Koyejo, Sanmi
Maximum Manifold Capacity Representations (MMCR) is a recent multi-view self-supervised learning (MVSSL) method that matches or surpasses other leading MVSSL methods. MMCR is intriguing because it does not fit neatly into any of the commonplace MVSSL
Externí odkaz:
http://arxiv.org/abs/2406.09366