Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Velipasalar, Senem"'
In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning a
Externí odkaz:
http://arxiv.org/abs/2405.09014
Functional near-infrared spectroscopy (fNIRS) is a non-intrusive way to measure cortical hemodynamic activity. Predicting cognitive workload from fNIRS data has taken on a diffuse set of methods. To be applicable in real-world settings, models are ne
Externí odkaz:
http://arxiv.org/abs/2405.00213
Gait is a behavioral biometric modality that can be used to recognize individuals by the way they walk from a far distance. Most existing gait recognition approaches rely on either silhouettes or skeletons, while their joint use is underexplored. Fea
Externí odkaz:
http://arxiv.org/abs/2404.10213
Publikováno v:
CVPR 2024
Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in interpreting the complex environment. However, a notable challenge has be
Externí odkaz:
http://arxiv.org/abs/2403.08919
Deep neural networks are extensively applied to real-world tasks, such as face recognition and medical image classification, where privacy and data protection are critical. Image data, if not protected, can be exploited to infer personal or contextua
Externí odkaz:
http://arxiv.org/abs/2402.09316
Autor:
Pan, Chenbin, Yaman, Burhaneddin, Nesti, Tommaso, Mallik, Abhirup, Allievi, Alessandro G, Velipasalar, Senem, Ren, Liu
Publikováno v:
CVPR2024
Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have recently achieved notable performance, through enhanced scene understa
Externí odkaz:
http://arxiv.org/abs/2401.05577
In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary random variable indicating whether the process is anomalous. To identi
Externí odkaz:
http://arxiv.org/abs/2312.00088
In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users. In particular, we propose a novel deep RL framework with multiple act
Externí odkaz:
http://arxiv.org/abs/2311.11206
Federated learning (FL) aims at keeping client data local to preserve privacy. Instead of gathering the data itself, the server only collects aggregated gradient updates from clients. Following the popularity of FL, there has been considerable amount
Externí odkaz:
http://arxiv.org/abs/2310.19222
Autor:
Hasan, Md Zahid, Chen, Jiajing, Wang, Jiyang, Rahman, Mohammed Shaiqur, Joshi, Ameya, Velipasalar, Senem, Hegde, Chinmay, Sharma, Anuj, Sarkar, Soumik
Recognizing the activities causing distraction in real-world driving scenarios is critical for ensuring the safety and reliability of both drivers and pedestrians on the roadways. Conventional computer vision techniques are typically data-intensive a
Externí odkaz:
http://arxiv.org/abs/2306.10159