Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively expensive for large modern architectures. Local methods, which have emerged as a popular alternative, focus on specific parameter regions that can be approximated by functions with tractable integrals. While these often yield satisfactory empirical resul

Statistical Analysis of Neural Data on Simons Foundation

Generate cat images with neural networks. Contribute to aleju/cat-generator development by creating an account on GitHub

The user discusses confusion around neural networks in trading, questioning their effectiveness compared to simpler systems like JMA. They mention normalization of indicators, correlation analysis, and the importance of finding a suitable trading system. They also seek Python syntax advice for handling data columns and numbers

Combining fMRI sentence patterns and neural networks to quantify contextual effects in the brain Since 2015 This research focuses on the development of computational models to study how the meaning of a word varies between different sentences, using fMRI data. Our hypothesis is based on the assumption that words or concepts are represented as a collection of individual features (attributes) localized on known brain areas/networks. The neural network architecture FGREP is applied to map Concept Attribute R

Wir freuen uns auf den Vortrag "Building Interpretable Neural Networks with Keras and LIME" von Dr. Shirin Glander, Data Scientist codecentric AG. Hier die thematische Übe

## Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel Abstract: Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point prediction is not enough: the uncertainty (i.e. risk or confidence) of that prediction must also be estimated. Standard NNs, which are most often used in suc

Abstract page for arXiv paper 2406.00048v3: Towards a theory of how the structure of language is acquired by deep neural networks

Neural coding is the process by which neurons represent and transmit information about sensory stimuli, motor commands, or internal states through patterns of action potentials, or spikes, in their fi

Today we're joined by Andrea Banino, a research scientist at DeepMind. In our conversation with Andrea, we explore his interest in artificial general intelligence by way...