12 papers · ranked by Valyu relevance
Tiago de Souza Farias, Jonas Maziero
Reversibility in artificial neural networks allows us to retrieve the input given an output. We present feature alignment, a method for approximating reversibility in arbitrary neural networks. We train a network by minimizing the distance between the output of a data point and the random output with respect to a…
Tatsuaki Wada, Antonio Maria Scarfone
We consider Onsager’s non-equilibrium thermodynamics from the perspective of the gradient flow in information geometry. Assuming Onsager’s reciprocal relations, we can regard his phenomenological equations as gradient-flow equations and develop two different gradient-flow models. We consider their features and their…
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Thorsten M. Leucker, Ines Valenta, Thomas Hellmut Schindler
Positron emission tomography/computed tomography (PET/CT) applied with positron-emitting flow tracers such as 13N-ammonia and 82Rubidium enables the quantification of both myocardial perfusion and myocardial blood flow (MBF) in milliliters per gram per minute for coronary artery disease (CAD) detection and…
Pengxi Fu, Zhen Wang, Jianxin Guo, Yushuai Zhang + 4 more
Modern communication systems increasingly leverage multiple information streams-including channel observations, statistical models, and contextual knowledge-to enhance decoding reliability. However, the varying and often unpredictable quality of these sources poses a critical challenge: rigid combination rules fail…
Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon
Complex flows are often characterized using the theory of Lagrangian coherent structures (LCS), which leverages the motion of flow-embedded tracers to highlight features of interest. LCS are commonly employed to study fluid mechanical systems where flow tracers are readily observed, but they are broadly applicable to…
Javier Cuadrado, Ulysse Rançon, Benoit R. Cottereau, Francisco Barranco + 1 more
'Francisco Barranco' 'Timothée Masquelier'] Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or “spikes”, when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent…
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni Brea + 1 more
'Johanni Brea' 'Francis Ouellette'] This is a PLOS Computational Biology Education paper. The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization…
Alexander Vidal, Samy Wu Fung, Luis Tenorio, Stanley Osher + 1 more
'Levon Nurbekyan'] A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of…
Guillaume Quétant, Yury Belousov, Vitaliy Kinakh, Slava Voloshynovskiy + 2 more
'Slava Voloshynovskiy' 'Sotiris Kotsiantis' 'Marco Piangerelli'] We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles of information bottleneck and bottleneck-based networks in the auto-encoding…
Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert
Information bottleneck (IB) is a technique for extracting information in one random variable X that is relevant for predicting another random variable Y. IB works by encoding X in a compressed “bottleneck” random variable M from which Y can be accurately decoded. However, finding the optimal bottleneck variable…
Chen-Hsiu Huang, Ja-Ling Wu, Jun Chen
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational…