7 papers · ranked by Valyu relevance
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…
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…
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…
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…
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
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…