Zobrazeno 1 - 10
of 22 159
pro vyhledávání: '"Laufer, A."'
We tackle the challenge of uncertainty quantification in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influenced by various factors such as noise and reverberation, leading to signi
Externí odkaz:
http://arxiv.org/abs/2409.11804
This work presents a novel approach to monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, symmetries, occlusions, and lack of annotated real-world data. The method leverages synth
Externí odkaz:
http://arxiv.org/abs/2407.12138
Autor:
Rubin, Or, Laufer, Shlomi
Surgical phase recognition is a key task in computer-assisted surgery, aiming to automatically identify and categorize the different phases within a surgical procedure. Despite substantial advancements, most current approaches rely on fully supervise
Externí odkaz:
http://arxiv.org/abs/2406.18481
Autor:
Huguet, Guillaume, Vuckovic, James, Fatras, Kilian, Thibodeau-Laufer, Eric, Lemos, Pablo, Islam, Riashat, Liu, Cheng-Hao, Rector-Brooks, Jarrid, Akhound-Sadegh, Tara, Bronstein, Michael, Tong, Alexander, Bose, Avishek Joey
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences. In this paper, we exploit the rich biological inductive bias of amin
Externí odkaz:
http://arxiv.org/abs/2405.20313
This work addresses the scattering problem of an incident wave at a junction connecting two semi-infinite waveguides, which we intend to solve using Physics-Informed Neural Networks (PINNs). As with other deep learning-based approaches, PINNs are kno
Externí odkaz:
http://arxiv.org/abs/2404.09794
In this paper, we introduce VoteCut, an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut employs normalized-cut based graph partitioning, clustering and a pixel v
Externí odkaz:
http://arxiv.org/abs/2403.07700
Autor:
Uzrad, Noy, Barkan, Oren, Elharar, Almog, Shvartzman, Shlomi, Laufer, Moshe, Wolf, Lior, Koenigstein, Noam
This paper presents DiffMoog - a differentiable modular synthesizer with a comprehensive set of modules typically found in commercial instruments. Being differentiable, it allows integration into neural networks, enabling automated sound matching, to
Externí odkaz:
http://arxiv.org/abs/2401.12570
The ability to locate and classify action segments in long untrimmed video is of particular interest to many applications such as autonomous cars, robotics and healthcare applications. Today, the most popular pipeline for action segmentation is compo
Externí odkaz:
http://arxiv.org/abs/2401.00438
With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses difficulties for users to filter useful posts and find important information. Tags provide a concise feature dimens
Externí odkaz:
http://arxiv.org/abs/2312.14279
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is to find a configuration that adheres to user-specified limits on certai
Externí odkaz:
http://arxiv.org/abs/2312.01692