Publications related to Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Related publications (22)

Optimizing fairness and utility in healthcare machine learning models

Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

2025

Neural Redshift: Random Networks are not Random Functions

Valentin Hartmann

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

IEEE2024

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

2024

On the atypical solutions of the symmetric binary perceptron

Lenka Zdeborová, Florent Gérard Krzakala, Damien Yves Valery Barbier

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically correspond ...

Iop Publishing Ltd2024

Statistical mechanics of deep learning

Freya Behrens

2024

Theory and applications of the sum-of-squares technique

Luca Pesce, Giovanni Piccioli, Elisabetta Cornacchia

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

2024

Socially Intelligent Networks: A framework for decision making over graphs

Ali H. Sayed

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

2024

Social-Transmotion: Promptable Human Trajectory Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao, Kaouther Messaoud Ben Amor

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

2024

Validating functional redundancy with mixed generative adversarial networks

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

ELSEVIER2023

Overview of Cryogenic Operation in Nanoscale Technology Nodes

Edoardo Charbon

Cryogenic operation is the backbone of many cutting edge applications. Significant work has been done to characterize and model all cryogenic effects for creating a reliable, robust and efficient cryogenic design across a wide range of technologies and pro ...

IEEE2023

Predicting nonlinear optical scattering with physics-driven neural networks

Demetri Psaltis, Carlo Gigli

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

AIP Publishing2023

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Vassily Hatzimanikatis, Ljubisa Miskovic, Subham Choudhury, Michaël Roger Germain Moret, Bharath Narayanan, David Michael Moret

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these da ...

Zenodo2023

Neural signatures of visuo-motor integration during human-robot interactions

Olaf Blanke, Hannes Bleuler, Giulio Rognini, Fosco Bernasconi, Silvia Marchesotti

Visuo-motor integration shapes our daily experience and underpins the sense of feeling in control over our actions. The last decade has seen a surge in robotically and virtually mediated interactions, whereby bodily actions ultimately result in an artifici ...

FRONTIERS MEDIA SA2023

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Jian Wang, Matthias Finger, Qian Wang, Yiming Li, João Miguel das Neves Duarte, Varun Sharma, Yi Zhang, Lei Zhang, Tian Cheng, Yixing Chen, Alexis Kalogeropoulos, Ioannis Papadopoulos, Hua Zhang, Siyuan Wang, Jessica Prisciandaro, Xin Chen, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Davide Di Croce, Guido Andreassi, Arvind Shah, Jian Zhao, Rakesh Chawla, Jan Steggemann, Konstantin Androsov, Federica Legger, Gabriele Grosso

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used i ...

American Physical Society2023

Personalized predictions of adverse side effects of the COVID-19 vaccines

Nahal Mansouri, Sahand Jamal Rahi

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was ...

ELSEVIER SCI LTD2023

Reed-Muller Codes

Emmanuel Abbé

Reed-Muller (RM) codes are among the oldest, simplest and perhaps most ubiquitous family of codes. They are used in many areas of coding theory in both electrical engineering and computer science. Yet, many of their important properties are still under inv ...

Emerald2023

Data-based model maintenance in the era of industry 4.0: A methodology

Dimitrios Kyritsis, Foivos Psarommatis Giannakopoulos

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A co ...

ELSEVIER SCI LTD2022

Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models

Michel Bierlaire, Nicola Marco Ortelli

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed ...

IEEE Institute of Electrical and Electronics Engineers2022

Bottlenecks, Modularity, and the Neural Control of Behavior

Riccardo Ravasio

In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals enco ...

FRONTIERS MEDIA SA2022

Towards Empathetic Open-Domain Chatbots

Yubo Xie

The research community of dialog generation has been interested in incorporating emotional information into the design of open-domain dialog systems ever since neural networks (sequence-to-sequence models in particular) were adopted for modeling dialogs. T ...

EPFL2022