Zobrazeno 1 - 10
of 71 786
pro vyhledávání: '"A Halil"'
Autor:
Pan, Hongyi, Hong, Ziliang, Durak, Gorkem, Keles, Elif, Aktas, Halil Ertugrul, Taktak, Yavuz, Medetalibeyoglu, Alpay, Zhang, Zheyuan, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Miller, Frank, Keswani, Rajesh N., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification ut
Externí odkaz:
http://arxiv.org/abs/2411.05697
Recent methods in quantile regression have adopted a classification perspective to handle challenges posed by heteroscedastic, multimodal, or skewed data by quantizing outputs into fixed bins. Although these regression-as-classification frameworks ca
Externí odkaz:
http://arxiv.org/abs/2411.01266
We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. Traditional reliance on labeled data has been a limitation for robotic
Externí odkaz:
http://arxiv.org/abs/2410.21926
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530
In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach i
Externí odkaz:
http://arxiv.org/abs/2410.21037
A growing number of machine learning scenarios rely on knowledge distillation where one uses the output of a surrogate model as labels to supervise the training of a target model. In this work, we provide a sharp characterization of this process for
Externí odkaz:
http://arxiv.org/abs/2410.18837
Autor:
Jha, Debesh, Susladkar, Onkar Kishor, Gorade, Vandan, Keles, Elif, Antalek, Matthew, Seyithanoglu, Deniz, Cebeci, Timurhan, Aktas, Halil Ertugrul, Kartal, Gulbiz Dagoglu, Kaymakoglu, Sabahattin, Erturk, Sukru Mehmet, Velichko, Yuri, Ladner, Daniela, Borhani, Amir A., Medetalibeyoglu, Alpay, Durak, Gorkem, Bagci, Ulas
Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications of portal hypertension, malignancy and death. Early diagno
Externí odkaz:
http://arxiv.org/abs/2410.16296
Motivation: The gut microbiota has recently emerged as a key factor that underpins certain connections between diet and human health. A tremendous amount of knowledge has been amassed from experimental studies on diet, human metabolism and microbiome
Externí odkaz:
http://arxiv.org/abs/2409.19581
Recently, the centerline has become a popular representation of lanes due to its advantages in solving the road topology problem. To enhance centerline prediction, we have developed a new approach called TopoMask. Unlike previous methods that rely on
Externí odkaz:
http://arxiv.org/abs/2409.11325
Training deep neural networks often requires large-scale datasets, necessitating storage and processing on cloud servers due to computational constraints. The procedures must follow strict privacy regulations in domains like healthcare. Split Learnin
Externí odkaz:
http://arxiv.org/abs/2407.08977