Advances in reinforcement learning (RL) often rely on massive compute
re...
It is doubtful that animals have perfect inverse models of their limbs (...
Learning in biological and artificial neural networks is often framed as...
Unlike robots, humans learn, adapt and perceive their bodies by interact...
Backpropagation of error (BP) is a widely used and highly successful lea...
The automation of probabilistic reasoning is one of the primary aims of
...
Understanding how perception and action deal with sensorimotor conflicts...
Deep neural network (DNN) is an indispensable machine learning tool for
...
We propose a virtual staining methodology based on Generative Adversaria...
The aim of probabilistic programming is to automatize every aspect of
pr...
This paper introduces the Indian Chefs Process (ICP), a Bayesian
nonpara...
Generative adversarial networks (GANs) are the state of the art in gener...
Visual object recognition is not a trivial task, especially when the obj...
Particle-based variational inference offers a flexible way of approximat...
Issues regarding explainable AI involve four components: users, laws &
r...
An important issue in neural network research is how to choose the numbe...
Here, we present a novel approach to solve the problem of reconstructing...
A fundamental goal in network neuroscience is to understand how activity...
Estimating the state of a dynamical system from a series of noise-corrup...
This workshop explores the interface between cognitive neuroscience and
...
We introduce a convolutional neural network for inferring a compact
dise...
Judgments about personality based on facial appearance are strong effect...