Spiking Neural Networks As Universal Function Approximators

The world of neuroscience has been abuzz with a groundbreaking discovery, shedding light on the enigmatic realm of psychedelic drugs and their impact on the human brain. In a recent study published in Nature Medicine, scientists have unraveled a unique 'neural fingerprint' associated with these mind

Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot leverage previously learned knowledge to help with learning new causal models. In order to tackle these challenges, we represent a novel algorithm called \textit{causal relational networks} (CRN

The world of neuroscience has been abuzz with a groundbreaking discovery, one that sheds light on the enigmatic realm of psychedelic drugs and their impact on the human brain. This revelation, dubbed the 'neural fingerprint' of psychedelics, has emerged from a comprehensive study, offering a unique

Statistical Analyses for omics data and machine learning using Galaxy tools

Machine Learning and Neural Network Applications in Engineering

Relational inductive biases, deep learning, and graph networks Battaglia et al., arXiv'18 Earlier this week we saw the argument that causal reasoning (where most of the interesting questions lie!) requires more than just associational machine learning. Structural causal models have at their core a graph of entities and relationships between them. Today we’ll be looking

Giacomo Tesio - A decompiler for artificial neural network

I encountered a problem with extremely large neural network that was created in KERAS, using Tensorflow backend. The memory footprint in one of the layer is already bigger than the size of current GPU memory (it has just

Python, neural network, Chainer