Zobrazeno 1 - 10
of 4 566
pro vyhledávání: '"Eryılmaz A"'
Reproducibility is essential for scientific research. However, in computer vision, achieving consistent results is challenging due to various factors. One influential, yet often unrecognized, factor is CUDA-induced randomness. Despite CUDA's advantag
Externí odkaz:
http://arxiv.org/abs/2410.02806
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
Autor:
Isil, Cagatay, Koydemir, Hatice Ceylan, Eryilmaz, Merve, de Haan, Kevin, Pillar, Nir, Mentesoglu, Koray, Unal, Aras Firat, Rivenson, Yair, Chandrasekaran, Sukantha, Garner, Omai B., Ozcan, Aydogan
Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable t
Externí odkaz:
http://arxiv.org/abs/2407.12337
Autor:
Zheng, Yilin, Eryilmaz, Atilla
With the development of edge networks and mobile computing, the need to serve heterogeneous data sources at the network edge requires the design of new distributed machine learning mechanisms. As a prevalent approach, Federated Learning (FL) employs
Externí odkaz:
http://arxiv.org/abs/2406.00150
Autor:
Cayci, Semih, Eryilmaz, Atilla
In this paper, we study a natural policy gradient method based on recurrent neural networks (RNNs) for partially-observable Markov decision processes, whereby RNNs are used for policy parameterization and policy evaluation to address curse of dimensi
Externí odkaz:
http://arxiv.org/abs/2405.18221
Autor:
Damm, Hendrik, Pakull, Tabea M. G., Eryılmaz, Bahadır, Becker, Helmut, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Schultenkämper, Sergej, Friedrich, Christoph M.
This study aims to leverage state of the art language models to automate generating the "Brief Hospital Course" and "Discharge Instructions" sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians' administrative workload. We i
Externí odkaz:
http://arxiv.org/abs/2405.11255
Autor:
Zhu, Feiwen, Nowaczynski, Arkadiusz, Li, Rundong, Xin, Jie, Song, Yifei, Marcinkiewicz, Michal, Eryilmaz, Sukru Burc, Yang, Jun, Andersch, Michael
AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary training code. OpenFold is the first trainable public reimp
Externí odkaz:
http://arxiv.org/abs/2404.11068
We introduce SwiftCache, a "fresh" learning-based caching framework designed for content distribution networks (CDNs) featuring distributed front-end local caches and a dynamic back-end database. Users prefer the most recent version of the dynamicall
Externí odkaz:
http://arxiv.org/abs/2402.17111
Autor:
Cayci, Semih, Eryilmaz, Atilla
We analyze recurrent neural networks with diagonal hidden-to-hidden weight matrices, trained with gradient descent in the supervised learning setting, and prove that gradient descent can achieve optimality \emph{without} massive overparameterization.
Externí odkaz:
http://arxiv.org/abs/2402.12241
Autor:
Eryilmaz, Merve, Goncharov, Artem, Han, Gyeo-Re, Joung, Hyou-Arm, Ballard, Zachary S., Ghosh, Rajesh, Zhang, Yijie, Di Carlo, Dino, Ozcan, Aydogan
Publikováno v:
ACS Nano (2024)
The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest
Externí odkaz:
http://arxiv.org/abs/2402.17774