Zobrazeno 1 - 10
of 504
pro vyhledávání: '"Taylor Sam"'
Random walks on expanders play a crucial role in Markov Chain Monte Carlo algorithms, derandomization, graph theory, and distributed computing. A desirable property is that they are rapidly mixing, which is equivalent to having a spectral gap $\gamma
Externí odkaz:
http://arxiv.org/abs/2412.13109
Autor:
Sharma, Harshita, Salvatelli, Valentina, Srivastav, Shaury, Bouzid, Kenza, Bannur, Shruthi, Castro, Daniel C., Ilse, Maximilian, Bond-Taylor, Sam, Ranjit, Mercy Prasanna, Falck, Fabian, Pérez-García, Fernando, Schwaighofer, Anton, Richardson, Hannah, Wetscherek, Maria Teodora, Hyland, Stephanie L., Alvarez-Valle, Javier
There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretat
Externí odkaz:
http://arxiv.org/abs/2411.11362
Autor:
Olesker-Taylor, Sam, Schmid, Dominik
We analyse the convergence to equilibrium of the Bernoulli--Laplace urn model: initially, one urn contains $k$ red balls and a second $n-k$ blue balls; in each step, a pair of balls is chosen uniform and their locations are switched. Cutoff is known
Externí odkaz:
http://arxiv.org/abs/2409.07900
Autor:
Olesker-Taylor, Sam
Publikováno v:
35th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2024) Volume 302, pp. 20:1-20:14
We extend the hardcore model to a multicoloured version: a subset of vertices of a graph are coloured such that no vertex is adjacent to one of the same colour; uncoloured vertices do not constrain neighbours. This mathematically models multi-channel
Externí odkaz:
http://arxiv.org/abs/2406.14376
Autor:
Olesker-Taylor, Sam, Zanetti, Luca
We present a theoretical analysis of the Elo rating system, a popular method for ranking skills of players in an online setting. In particular, we study Elo under the Bradley--Terry--Luce model and, using techniques from Markov chain theory, show tha
Externí odkaz:
http://arxiv.org/abs/2406.05869
Autor:
Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Schwaighofer, Anton, Thieme, Anja, Bond-Taylor, Sam, Ilse, Maximilian, Pérez-García, Fernando, Salvatelli, Valentina, Sharma, Harshita, Meissen, Felix, Ranjit, Mercy, Srivastav, Shaury, Gong, Julia, Codella, Noel C. F., Falck, Fabian, Oktay, Ozan, Lungren, Matthew P., Wetscherek, Maria Teodora, Alvarez-Valle, Javier, Hyland, Stephanie L.
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution. However, to impact clinical practice, models must achieve a high
Externí odkaz:
http://arxiv.org/abs/2406.04449
Autor:
Archer, Eleanor, Hartarsky, Ivailo, Kolesnik, Brett, Olesker-Taylor, Sam, Schapira, Bruno, Valesin, Daniel
In Catalan percolation, all nearest-neighbor edges $\{i,i+1\}$ along $\mathbb Z$ are initially occupied, and all other edges are open independently with probability $p$. Open edges $\{i,j\}$ are occupied if some pair of edges $\{i,k\}$ and $\{k,j\}$,
Externí odkaz:
http://arxiv.org/abs/2404.19583
Autor:
Pérez-García, Fernando, Sharma, Harshita, Bond-Taylor, Sam, Bouzid, Kenza, Salvatelli, Valentina, Ilse, Maximilian, Bannur, Shruthi, Castro, Daniel C., Schwaighofer, Anton, Lungren, Matthew P., Wetscherek, Maria, Codella, Noel, Hyland, Stephanie L., Alvarez-Valle, Javier, Oktay, Ozan
Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal systems within the computer vision and medical imaging domains. However,
Externí odkaz:
http://arxiv.org/abs/2401.10815
Autor:
Pérez-García, Fernando, Bond-Taylor, Sam, Sanchez, Pedro P., van Breugel, Boris, Castro, Daniel C., Sharma, Harshita, Salvatelli, Valentina, Wetscherek, Maria T. A., Richardson, Hannah, Lungren, Matthew P., Nori, Aditya, Alvarez-Valle, Javier, Oktay, Ozan, Ilse, Maximilian
Publikováno v:
European Conference on Computer Vision (ECCV) 2024
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shi
Externí odkaz:
http://arxiv.org/abs/2312.12865