Zobrazeno 1 - 10
of 2 318
pro vyhledávání: '"A. Ehteshami"'
Autor:
A. Ehteshami, M. Varmazyar
Publikováno v:
Wind Energy Science, Vol 8, Pp 1771-1793 (2023)
In the realm of novel technologies for generating electricity from renewable resources, an emerging category of wind energy converters called airborne wind energy systems (AWESs) has gained prominence. These pioneering systems employ tethered wings o
Externí odkaz:
https://doaj.org/article/33a03054febf426d8631fb0ebf537e47
Autor:
Cai, Ruisi, Ro, Yeonju, Kim, Geon-Woo, Wang, Peihao, Bejnordi, Babak Ehteshami, Akella, Aditya, Wang, Zhangyang
The proliferation of large language models (LLMs) has led to the adoption of Mixture-of-Experts (MoE) architectures that dynamically leverage specialized subnetworks for improved efficiency and performance. Despite their benefits, MoE models face sig
Externí odkaz:
http://arxiv.org/abs/2410.19123
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava
Autor:
Azarafza, Mehdi, Idrees, Fatima, Bejnordi, Ali Ehteshami, Steinmetz, Charles, Henkler, Stefan, Rettberg, Achim
Traffic Sign Recognition (TSR) detection is a crucial component of autonomous vehicles. While You Only Look Once (YOLO) is a popular real-time object detection algorithm, factors like training data quality and adverse weather conditions (e.g., heavy
Externí odkaz:
http://arxiv.org/abs/2410.05096
Autor:
Bejnordi, Babak Ehteshami, Kumar, Gaurav, Royer, Amelie, Louizos, Christos, Blankevoort, Tijmen, Ghafoorian, Mohsen
Jointly learning multiple tasks with a unified model can improve accuracy and data efficiency, but it faces the challenge of task interference, where optimizing one task objective may inadvertently compromise the performance of another. A solution to
Externí odkaz:
http://arxiv.org/abs/2402.16848
Autor:
Bergner, Benjamin, Skliar, Andrii, Royer, Amelie, Blankevoort, Tijmen, Asano, Yuki, Bejnordi, Babak Ehteshami
Large language models (LLMs) have become ubiquitous in practice and are widely used for generation tasks such as translation, summarization and instruction following. However, their enormous size and reliance on autoregressive decoding increase deplo
Externí odkaz:
http://arxiv.org/abs/2402.16844
Training a single model on multiple input domains and/or output tasks allows for compressing information from multiple sources into a unified backbone hence improves model efficiency. It also enables potential positive knowledge transfer across tasks
Externí odkaz:
http://arxiv.org/abs/2310.08910
The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content. However, processing uniform background areas of an image should not necessitate as
Externí odkaz:
http://arxiv.org/abs/2307.02321
Autor:
Royer, Amelie, Karmanov, Ilia, Skliar, Andrii, Bejnordi, Babak Ehteshami, Blankevoort, Tijmen
Mixture of Experts (MoE) are rising in popularity as a means to train extremely large-scale models, yet allowing for a reasonable computational cost at inference time. Recent state-of-the-art approaches usually assume a large number of experts, and r
Externí odkaz:
http://arxiv.org/abs/2304.05497
Autor:
Hosseini, Mahdi S., Bejnordi, Babak Ehteshami, Trinh, Vincent Quoc-Huy, Hasan, Danial, Li, Xingwen, Kim, Taehyo, Zhang, Haochen, Wu, Theodore, Chinniah, Kajanan, Maghsoudlou, Sina, Zhang, Ryan, Yang, Stephen, Zhu, Jiadai, Chan, Lyndon, Khaki, Samir, Buin, Andrei, Chaji, Fatemeh, Salehi, Ala, Nguyen, Bich Ngoc, Samaras, Dimitris, Plataniotis, Konstantinos N.
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digita
Externí odkaz:
http://arxiv.org/abs/2304.05482
High-resolution images are widely adopted for high-performance object detection in videos. However, processing high-resolution inputs comes with high computation costs, and naive down-sampling of the input to reduce the computation costs quickly degr
Externí odkaz:
http://arxiv.org/abs/2204.02397