Deep Learning

Modelling Coloured Images

This post is presented in noteworthy - the journal blog. Hi everybody! Today, we will continue the series about autoregressive models. Summary Autoregressive models — PixelCNN Modelling coloured images PixelCNN’s blind spot in the receptive field Fixing the blind spot — Gated PixelCNN Conditional generation with Gated PixelCNN Gated PixelCNN with cropped convolutions Improving performance — PixelCNN++ Improving sampling time — Fast PixelCNN++ Using attention mechanisms — PixelSNAIL Generating Diverse High-Fidelity Images — VQ-VAE 2 For each topic, we implemented the models that are available in this repository.

Autoregressive Models — PixelCNN

This post is presented in towardsdatascience.com. Hi everybody! This is our first post of a series about modern autoregressive models. Here are the topics we are going to cover in this series:

Building new methods with Bayesian Optimization

Exploring Bayesian Optimization as a tool for exploitation of configurational spaces in Neuroscience

Normative Modelling in Psychiatry

Using Auto-regressive models to identify out-of-distribution brain data to identify disorders

Modelling Behaviour

Using neural networks to simulate human performance and interpret the model's trained weights

Bayesian Optimization for real-time, automatic design of face stimuli in human-centred research

Investigating the cognitive and neural mechanisms involved with face processing is a fundamental task in modern neuroscience and psychology. To date, the majority of such studies have focused on the use of pre-selected stimuli. The absence of …

Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including …

Elucidating Cognitive Processes Using LSTMs

Despite several decades of functional neuroimaging research the relationship between brain networks and cognition remains elusive. This is because the taxonomy of cognitive processes was developed largely blind to the functional organization of the …

Application of Artificial Neural Networks for modelling cognitive dimensions

The relationship between the brain and cognition remains unclear, despite several decades of functional neuroimaging research. One limitation is that the cognitive processes we attempt to match to brain activity are taken from psychological …