A very very brief introduction to neural network for physicists

# Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks Hyeonseob Nam, Hyo-Eun Kim Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc. Although these variations have been deemed to be implicitly handled by more training data and deeper networks, recent advances in image style transfer suggest that it is also possible to explicitly manipulate the style information. Extending this idea

A network with more parameters than training examples can memorize random labels perfectly. The same network, on real data, generalizes. Classical learning…

Abstract page for arXiv paper 2007.01930: Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data

The paper rigorously analyzes how superposition in neural networks confounds raw-activation alignment metrics using theory, simulations, and empirical data

Learn how Deep Learning models make their predictions

Measure functional connectivity between in vitro neural circuits using MEA electrophysiology for neural innervation and co-culture research

Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks

pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

### nnet Feed-Forward Neural Networks and Multinomial Log-Linear Models Search the nnet package 57 4 6 - class.ind: Generates Class Indicator Matrix from a Factor - multinom: Fit Multinomial Log-linear Models - nnet: Fit Neural Networks - nnet.Hess: Evaluates Hessian for a Neural Network - predict.nnet: Predict New Examples by a Trained Neural Net - which.is.max: Find Maximum Position in Vector - Browse all... nnet multinom: Fit Multinomial Log-linear Models # multinom: Fit Multinomial Log-linear M