Zobrazeno 1 - 10
of 55 078
pro vyhledávání: '"HUBERT, P."'
Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
Autor:
Gonzalez-Perez, Ruben, Lopez-Tiro, Francisco, Reyes-Amezcua, Ivan, Falcon-Morales, Eduardo, Rodriguez-Gueant, Rosa-Maria, Hubert, Jacques, Daudon, Michel, Ochoa-Ruiz, Gilberto, Daul, Christian
Publikováno v:
2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)
Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has f
Externí odkaz:
http://arxiv.org/abs/2409.13409
Autor:
Hebert, Liam, Kyriakidi, Marialena, Pham, Hubert, Sayana, Krishna, Pine, James, Sodhi, Sukhdeep, Jash, Ambarish
Hybrid recommender systems, combining item IDs and textual descriptions, offer potential for improved accuracy. However, previous work has largely focused on smaller datasets and model architectures. This paper introduces Flare (Fusing Language model
Externí odkaz:
http://arxiv.org/abs/2409.11699
Social media platforms enable users to share diverse types of information, including geolocation data that captures their movement patterns. Such geolocation data can be leveraged to reconstruct the trajectory of a user's visited Points of Interest (
Externí odkaz:
http://arxiv.org/abs/2409.11301
This paper presents DENSER, an efficient and effective approach leveraging 3D Gaussian splatting (3DGS) for the reconstruction of dynamic urban environments. While several methods for photorealistic scene representations, both implicitly using neural
Externí odkaz:
http://arxiv.org/abs/2409.10041
In the area of large-scale training of graph embeddings, effective training frameworks and partitioning methods are critical for handling large networks. However, they face two major challenges: 1) existing synchronized distributed frameworks require
Externí odkaz:
http://arxiv.org/abs/2409.09887
Autor:
Zhu, Max, Yao, Jian, Mynatt, Marcus, Pugzlys, Hubert, Li, Shuyi, Bacallado, Sergio, Zhao, Qingyuan, Jia, Chunjing
We introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the next observation, the algorithm iteratively determines the most infor
Externí odkaz:
http://arxiv.org/abs/2409.07042
The uptake of formalized prior elicitation from experts in Bayesian clinical trials has been limited, largely due to the challenges associated with complex statistical modeling, the lack of practical tools, and the cognitive burden on experts require
Externí odkaz:
http://arxiv.org/abs/2409.05271
Autor:
Ruczyński, Hubert, Kozak, Anna
The majority of automated machine learning (AutoML) solutions are developed in Python, however a large percentage of data scientists are associated with the R language. Unfortunately, there are limited R solutions available. Moreover high entry level
Externí odkaz:
http://arxiv.org/abs/2409.04789
Context. Dust coagulation and fragmentation impact the structure and evolution of protoplanetary disks and set the initial conditions for planet formation. Dust grains dominate the opacities, they determine the cooling times of the gas, they influenc
Externí odkaz:
http://arxiv.org/abs/2409.03816
To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses other compression techniques. Despite this, existing adversarial attack
Externí odkaz:
http://arxiv.org/abs/2409.01282