most citedBenchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20231 cited

Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation

Lucia Innocenti, Michela Antonelli, Francesco Cremonesi +5

Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use…

q-bio.GN2023

Tackling the dimensions in imaging genetics with CLUB-PLS

Andre Altmann, Ana C Lawry Aguila, Neda Jahanshad +2

A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brai…

cs.LG2023

On Tail Decay Rate Estimation of Loss Function Distributions

Etrit Haxholli, Marco Lorenzi

The study of loss function distributions is critical to characterize a model's behaviour on a given machine learning problem. For example, while the quality of a model is commonly…

cs.LG2023

Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows

Etrit Haxholli, Marco Lorenzi

While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transfo…

cs.LG2023

Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching

Etrit Haxholli, Marco Lorenzi

In Diffusion Probabilistic Models (DPMs), the task of modeling the score evolution via a single time-dependent neural network necessitates extended training periods and may potenti…