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11/11/2022
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Advances in reinforcement learning (RL) often rely on massive compute re...
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09/16/2022
Learning Policies for Continuous Control via Transition Models
It is doubtful that animals have perfect inverse models of their limbs (...
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03/22/2022
Constrained Parameter Inference as a Principle for Learning
Learning in biological and artificial neural networks is often framed as...
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05/10/2021
Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception
Unlike robots, humans learn, adapt and perceive their bodies by interact...
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02/23/2021
Scaling up learning with GAIT-prop
Backpropagation of error (BP) is a widely used and highly successful lea...
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02/09/2021
Automatic variational inference with cascading flows
The automation of probabilistic reasoning is one of the primary aims of ...
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08/17/2020
A deep active inference model of the rubber-hand illusion
Understanding how perception and action deal with sensorimotor conflicts...
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04/30/2020
Explainable Deep Learning: A Field Guide for the Uninitiated
Deep neural network (DNN) is an indispensable machine learning tool for ...
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03/17/2020
Virtual staining for mitosis detection in Breast Histopathology
We propose a virtual staining methodology based on Generative Adversaria...
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02/03/2020
Automatic structured variational inference
The aim of probabilistic programming is to automatize every aspect of pr...
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01/29/2020
The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...
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07/09/2019
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
Generative adversarial networks (GANs) are the state of the art in gener...
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03/25/2019
The functional role of cue-driven feature-based feedback in object recognition
Visual object recognition is not a trivial task, especially when the obj...
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11/07/2018
Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference
Particle-based variational inference offers a flexible way of approximat...
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03/20/2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Issues regarding explainable AI involve four components: users, laws & r...
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02/10/2018
Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability
An important issue in neural network research is how to choose the numbe...
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05/19/2017
Deep adversarial neural decoding
Here, we present a novel approach to solve the problem of reconstructing...
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05/16/2017
GP CaKe: Effective brain connectivity with causal kernels
A fundamental goal in network neuroscience is to understand how activity...
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02/17/2017
Estimating Nonlinear Dynamics with the ConvNet Smoother
Estimating the state of a dynamical system from a series of noise-corrup...
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01/06/2017
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
This workshop explores the interface between cognitive neuroscience and ...
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10/12/2016
Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact dise...
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12/04/2015