16 papers · ranked by Valyu relevance
Fernando Pérez-García, Rachel Sparks, Sébastien Ourselin
Title: Highlights 1. • Open-source Python library for preprocessing, augmentation and sampling of medical images for deep learning. 2. • Support for 2D, 3D and 4D images such as X-ray, histopathology, CT, ultrasound and diffusion MRI. 3. • Modular design inspired by the deep learning framework PyTorch. 4. • Focus on…
JohnMark Taylor, Nikolaus Kriegeskorte
Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures…
Ovidiu-Constantin Novac, Mihai Cristian Chirodea, Cornelia Mihaela Novac, Nicu Bizon + 5 more
'Cornelia Mihaela Novac' 'Nicu Bizon' 'Mihai Oproescu' 'Ovidiu Petru Stan' 'Cornelia Emilia Gordan' 'Nikolaos Doulamis' 'Marcin Woźniak'] In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can…
Sean P. Kinahan, Julie M. Liss, Visar Berisha, Viacheslav Kovtun
The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology…
JohnMark Taylor, Nikolaus Kriegeskorte
Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures…
Mathieu Fourment, Matthew Macaulay, Christiaan J Swanepoel, Xiang Ji + 3 more
Bayesian inference has predominantly relied on the Markov chain Monte Carlo (MCMC) algorithm for many years. However, MCMC is computationally laborious, especially for complex phylogenetic models of time trees. This bottleneck has led to the search for alternatives, such as variational Bayes, which can scale better to…
Jacob Huth, Timothée Masquelier, Angelo Arleo
We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, [32])…
Christoph Linse, Hammam Alshazly, Thomas Martinetz
Within the last decade Deep Learning has become a tool for solving challenging problems like image recognition. Still, Convolutional Neural Networks (CNNs) are considered black-boxes, which are difficult to understand by humans. Hence, there is an urge to visualize CNN architectures, their internal processes and what…
Gabriele Orlando, Luis Serrano, Joost Schymkowitz, Frederic Rousseau + 1 more
AlphaFold () started a revolution in computational biology by allowing researchers to access reliable structural information for virtually any natural protein. The general protein folding problem, however, is still far from being solved: while we can predict the conformations of well-studied proteins, inferring the…
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini, Timothée Masquelier
'Timothée Masquelier'] Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of the existing SNN simulation frameworks are not…
Hassam Tahir, Eun-Sung Jung, Petros Daras
This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading…
Robert Rosenbaum, Gennady S. Cymbalyuk
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to…
Antonina Dobrowolska, Julian Świerczyński, Paweł Tecmer, Emil Sujkowski + 5 more
Python Frameworks for Next-Generation GPUs: A Comparative Study of CuPy and PyTorch on the Hopper and Grace Hopper Architecture Authors: Antonina Dobrowolska, Julian Świerczyński, Paweł Tecmer, Emil Sujkowski, Somayeh Ahmadkhani, Grzegorz Mazur, Klemens Noga, Jeff Hammond, Katharina Boguslawski In this work, we…
Marius Vieth, Ali Rahimi, Ashena Gorgan Mohammadi, Jochen Triesch + 1 more
'Mohammad Ganjtabesh'] Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target application areas. Among these, PymoNNto is a…
Richard Gast, Thomas R. Knösche, Ann Kennedy, Daniele Marinazzo
The mathematical study of real-world dynamical systems relies on models composed of differential equations. Numerical methods for solving and analyzing differential equation systems are essential when complex biological problems have to be studied, such as the spreading of a virus, the evolution of competing species in…
Jacob Tjaden, Brian Tjaden, Stephen R. Piccolo
The democratization of machine learning is a popular and growing movement. In a world with a wealth of publicly available data, it is important that algorithms for analysis of data are accessible and usable by everyone. We present MLpronto, a system for machine learning analysis that is designed to be easy to use so as…