Results 71 to 80 of about 3,905,952 (289)

Triple equivalence for the emergence of biological intelligence

Communications Physics
Intelligent algorithms developed evolutionarily within neural systems are considered in this work. Mathematical analyses unveil a triple equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable ...
Takuya Isomura
doaj   +1 more source

Bayesian Neural Networks via MCMC: A Python-Based Tutorial

IEEE Access
Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
doaj   +1 more source

Functional Variational Bayesian Neural Networks

CoRR, 2019
ICLR ...
Shengyang Sun, Guodong Zhang 0006, Jiaxin Shi, Roger B. Grosse   +3 more
openaire   +4 more sources

Photovoltaic Stimulation of Mouse and Pig Retina With Pyrolytic Carbon Microelectrodes Integrated on High‐Density Silicon Solar Cell Arrays

Advanced Healthcare Materials, EarlyView.
In this study, we fabricate photovoltaic retinal implants with pyrolytic carbon electrodes and test their ability to stimulate mouse and pig retinas when illuminated with near‐infrared light. We find increased spiking activity in both animal models compared to spontaneous activity and TTX controls and analyze the spike amplitude, latency and frequency.
Akihiro Matsumoto, Pratik Kusumanchi, Jesper Guldsmed Madsen, Asbjørn Cortnum Jørgensen, Gisele Alves dos Reis Benatto, Sune Thorsteinsson, Peter Behrensdorff Poulsen, Keisuke Yonehara, Toke Bek, Stephan Sylvest Keller, Rasmus Schmidt Davidsen   +10 more
wiley   +1 more source

Restricted Bayesian Neural Network

CoRR
Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing ...
Ganguly, Sourav, Bhattacharjee, Saprativa   +1 more
openaire   +3 more sources

Data Subsampling for Bayesian Neural Networks

CoRR, 2022
Markov Chain Monte Carlo (MCMC) algorithms do not scale well for large datasets leading to difficulties in Neural Network posterior sampling. In this paper, we propose Penalty Bayesian Neural Networks - PBNNs, as a new algorithm that allows the evaluation of the likelihood using subsampled batch data (mini-batches) in a Bayesian inference context ...
Kawasaki, Eiji, Holzmann, Markus, Adu-Gyamfi, Lawrence   +2 more
openaire   +2 more sources

Learning representations in Bayesian Confidence Propagation neural networks

, 2020
Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years. In this work we study biologically inspired unsupervised strategies in neural networks based on local Hebbian
Anders Lansner, Lansner, Anders,, Pawel Herman, Herman, Pawel,, Naresh Balaji Ravichandran, Ravichandran, Naresh Balaji,   +5 more
core   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Bayesian and information-theoretic tools for neuroscience [PDF]

, 2007
The overarching purpose of the studies presented in this report is the exploration of the uses of information theory and Bayesian inference applied to neural codes. Two approaches were taken: Starting from first principles, a coding mechanism is proposed,
Endres, Dominik M.
core   +2 more sources

Probabilistic classification of quality of service in wireless computer networks

ICT Express, 2019
There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as
Abdussalam Salama, Reza Saatchi
doaj   +1 more source