Zobrazeno 1 - 10
of 45 526
pro vyhledávání: '"Sharifi, A."'
Autor:
Saeedinia, Samaneh Alsadat, Sharifi, Mojtaba, Hosseindokht, Seyed Mohammad, Jafarpourdavatgar, Hedieh
This paper introduces an innovative method for ensuring global stability in a broad array of nonlinear systems. The novel approach enhances the traditional analysis based on Jacobian matrices by incorporating the Taylor series boundary error of estim
Externí odkaz:
http://arxiv.org/abs/2408.02985
Autor:
Nazari, Ali A., Catania, Jeremy, Sadeghian, Soroush, Jalali, Amir, Masnavi, Houman, Janabi-Sharifi, Farrokh, Zareinia, Kourosh
Minimally invasive robotic surgery has gained significant attention over the past two decades. Telerobotic systems, combined with robot-mediated minimally invasive techniques, have enabled surgeons and clinicians to mitigate radiation exposure for me
Externí odkaz:
http://arxiv.org/abs/2407.13162
Recent advancements in deep learning (DL) have significantly advanced medical image analysis. In the field of medical image processing, particularly in histopathology image analysis, the variation in staining protocols and differences in scanners pre
Externí odkaz:
http://arxiv.org/abs/2406.15685
Tracking a target in cluttered and dynamic environments is challenging but forms a core component in applications like aerial cinematography. The obstacles in the environment not only pose collision risk but can also occlude the target from the field
Externí odkaz:
http://arxiv.org/abs/2406.14639
Despite attaining high empirical generalization, the sharpness of models trained with sharpness-aware minimization (SAM) do not always correlate with generalization error. Instead of viewing SAM as minimizing sharpness to improve generalization, our
Externí odkaz:
http://arxiv.org/abs/2406.06700
A key challenge that threatens the widespread use of neural networks in safety-critical applications is their vulnerability to adversarial attacks. In this paper, we study the second-order behavior of continuously differentiable deep neural networks,
Externí odkaz:
http://arxiv.org/abs/2406.05119
Autor:
Sharifi, Sina, Fazlyab, Mahyar
We propose a novel reachability analysis method tailored for neural networks with differentiable activations. Our idea hinges on a sound abstraction of the neural network map based on first-order Taylor expansion and bounding the remainder. To this e
Externí odkaz:
http://arxiv.org/abs/2406.04476
Publikováno v:
Proceedings of the Conference on Robots and Vision (2024)
In this work, a deep learning-based technique is used to study the image-to-joint inverse kinematics of a tendon-driven supportive continuum arm. An eye-off-hand configuration is considered by mounting a camera at a fixed pose with respect to the ine
Externí odkaz:
http://arxiv.org/abs/2405.20248
Autor:
Li, Chuanhao, Yang, Runhan, Li, Tiankai, Bafarassat, Milad, Sharifi, Kourosh, Bergemann, Dirk, Yang, Zhuoran
Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. However, their application in strategic multi-agent decision-making environments is hampere
Externí odkaz:
http://arxiv.org/abs/2405.16376
Autor:
Rasekh, Ali, Heidari, Reza, Rezaie, Amir Hosein Haji Mohammad, Sedeh, Parsa Sharifi, Ahmadi, Zahra, Mitra, Prasenjit, Nejdl, Wolfgang
With the increasing availability of diverse data types, particularly images and time series data from medical experiments, there is a growing demand for techniques designed to combine various modalities of data effectively. Our motivation comes from
Externí odkaz:
http://arxiv.org/abs/2405.15442