Luca Ambrogioni | DeepAI

DeepAI AI Chat

Featured Co-authors

Philippe Giguère
32 publications
François Laviolette
31 publications
Marcel van Gerven
22 publications
Umut Güçlü
18 publications
Marcel A. J. van Gerven
18 publications
Brahim Chaib-draa
14 publications
Nasir Ahmad
13 publications
Eric Maris
9 publications
Yağmur Güçlütürk
9 publications
Dave Moore
8 publications
Max Hinne
6 publications

∙ 05/31/2023

Spontaneous symmetry breaking in generative diffusion models

Generative diffusion models have recently emerged as a leading approach ...

2 Gabriel Raya, et al. ∙

∙ 05/19/2022

Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers

In this work, we provide an exact likelihood alternative to the variatio...

14 Gianluigi Silvestri, et al. ∙

∙ 10/12/2021

Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling

Normalizing flows have shown great success as general-purpose density es...

2 Gianluigi Silvestri, et al. ∙

∙ 09/17/2021

Knowledge is reward: Learning optimal exploration by predictive reward cashing

There is a strong link between the general concept of intelligence and t...

12 Luca Ambrogioni, et al. ∙

∙ 02/23/2021

Scaling up learning with GAIT-prop

Backpropagation of error (BP) is a widely used and highly successful lea...

0 Sander Dalm, et al. ∙

∙ 02/09/2021

Automatic variational inference with cascading flows

The automation of probabilistic reasoning is one of the primary aims of ...

0 Luca Ambrogioni, et al. ∙

∙ 06/29/2020

Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations

In this paper we introduce the temporally factorized 3D convolution (3TC...

70 Gabrielle Ras, et al. ∙

∙ 06/11/2020

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

Traditional backpropagation of error, though a highly successful algorit...

1 Nasir Ahmad, et al. ∙

∙ 03/09/2020

Spike-Timing-Dependent Inference of Synaptic Weights

A potential solution to the weight transport problem, which questions th...

0 Nasir Ahmad, et al. ∙

∙ 02/03/2020

Automatic structured variational inference

The aim of probabilistic programming is to automatize every aspect of pr...

0 Luca Ambrogioni, et al. ∙

∙ 01/29/2020

The Indian Chefs Process

This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...

16 Patrick Dallaire, et al. ∙

∙ 12/20/2019

Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks

Perceived personality traits attributed to an individual do not have to ...

0 Gabrielle Ras, et al. ∙

∙ 12/09/2019

Temporal Factorization of 3D Convolutional Kernels

3D convolutional neural networks are difficult to train because they are...

0 Gabrielle Ras, et al. ∙

∙ 11/15/2019

Causal inference using Bayesian non-parametric quasi-experimental design

The de facto standard for causal inference is the randomized controlled ...

22 Max Hinne, et al. ∙

∙ 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...

1 Luca Ambrogioni, et al. ∙

∙ 03/31/2019

Perturbative estimation of stochastic gradients

In this paper we introduce a family of stochastic gradient estimation te...

0 Luca Ambrogioni, et al. ∙

∙ 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...

0 Luca Ambrogioni, et al. ∙

∙ 05/29/2018

Forward Amortized Inference for Likelihood-Free Variational Marginalization

In this paper, we introduce a new form of amortized variational inferenc...

0 Luca Ambrogioni, et al. ∙

∙ 05/29/2018

Wasserstein Variational Inference

This paper introduces Wasserstein variational inference, a new form of a...

0 Luca Ambrogioni, et al. ∙

∙ 05/19/2017

The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

This paper introduces the kernel mixture network, a new method for nonpa...

0 Luca Ambrogioni, et al. ∙

∙ 05/16/2017

GP CaKe: Effective brain connectivity with causal kernels

A fundamental goal in network neuroscience is to understand how activity...

0 Luca Ambrogioni, et al. ∙

∙ 04/10/2017

Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis

Computing accurate estimates of the Fourier transform of analog signals ...

0 Luca Ambrogioni, et al. ∙

∙ 02/17/2017

Estimating Nonlinear Dynamics with the ConvNet Smoother

Estimating the state of a dynamical system from a series of noise-corrup...

0 Luca Ambrogioni, et al. ∙

∙ 10/31/2016

Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes

The analysis of nonstationary time series is of great importance in many...

0 Luca Ambrogioni, et al. ∙

∙ 05/09/2016

Dynamic Decomposition of Spatiotemporal Neural Signals

Neural signals are characterized by rich temporal and spatiotemporal dyn...

0 Luca Ambrogioni, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world