In this article, we will apply the concept of multi-label multi-class classification with neural networks from the last post, to classify movie posters by genre. First we import the usual suspects in python. import numpy as np import pandas as pd import glob import scipy

Deep neural networks using multimodal vision-language models outperform unimodal models in predicting SEEG recordings, identifying sites of multimodal integration in the

An in-depth exploration of the architecture and applications of LSTM networks NLP

Feb 24, 2026 1 min read Papers Noise-induced patterns in neurobiological networks Starting from an identical initial condition, a spiral wave is sustained for sufficiently large noise intensity (sigma = 0.45), but it disappears in the absence of noise (sigma = 0) A new paper by ACDC member Daniele Avitabile and ACDC visitor James MacLaurin has been published on SIAM Journal of Applied Mathematics. The paper studies large networks of firing-rate neurons, subject to noise and to random synaptic connections. I

This paper reviews spiking neural network models that boost image classification using biologically plausible neurons, efficient coding, and optimized learning rules

Linear regression using PyTorch

while my_mcmc: gently(samples) Hierarchical Bayesian Neural Networks with Informative Priors stats NeuralNetworks Author Thomas Wiecki Published August 13, 2018 Imagine you have a machine learning (ML) problem but only small data (gasp, yes, this does exist). This often happens when your data set is nested – you might have many data points, but only few per category. For example, in ad-tech you may want predict how likely a user will buy a certain product. There could be thousands of products but you only

In early September, my PhD student Marta Andronic will be off to Turin to present our latest work "NeuraLUT: Hiding Neural Network Density in Boolean Synthesizable Functions" at the Field-Programmable Logic and Applications conference. Ahead of the detailed presentation at the conference, this blog post provides a short accessible summary of her exciting work. In

How a TensorFlow program is structured, what the training loop actually repeats, and how to tell a model that is learning from one that is memorising.

Master activation functions in neural networks: ReLU, sigmoid, tanh, GELU, Swish, and Mish. Learn how activation functions enable deep learning models to learn