Search Results for author: Ricardo Pio Monti

Found 15 papers, 5 papers with code

CAGE: Probing Causal Relationships in Deep Generative Models

no code implementations29 Sep 2021 Joey Bose, Ricardo Pio Monti, Aditya Grover

Deep generative models excel at generating complex, high-dimensional data, often exhibiting impressive generalization beyond the training distribution.

Robust classification Synthetic Data Generation

Causal Autoregressive Flows

2 code implementations4 Nov 2020 Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen

We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.

Causal Discovery Causal Inference +1

Bayesian optimization for automatic design of face stimuli

1 code implementation20 Jul 2020 Pedro F. da Costa, Romy Lorenz, Ricardo Pio Monti, Emily Jones, Robert Leech

Formally, we employ Bayesian optimization to efficiently search the latent space of state-of-the-art GAN models, with the aim to automatically generate novel faces, to maximize an individual subject's response.

Bayesian Optimization

Autoregressive flow-based causal discovery and inference

2 code implementations18 Jul 2020 Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvarinen

We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.

Causal Discovery Causal Inference +1

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

1 code implementation NeurIPS 2020 Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen

We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional energy-based models are unique in function space, up to a simple transformation.

Transfer Learning

Variational Autoencoders and Nonlinear ICA: A Unifying Framework

2 code implementations10 Jul 2019 Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen

We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement.

Disentanglement

Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA

no code implementations19 Apr 2019 Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen

We consider the problem of inferring causal relationships between two or more passively observed variables.

Causal Discovery

A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data

no code implementations24 May 2018 Ricardo Pio Monti, Aapo Hyvärinen

We propose a probabilistic model which simultaneously performs both a grouping of variables (i. e., detecting community structure) and estimation of connectivities between the groups which correspond to latent variables.

Connectivity Estimation

Avoiding degradation in deep feed-forward networks by phasing out skip-connections

no code implementations ICLR 2018 Ricardo Pio Monti, Sina Tootoonian, Robin Cao

A widely observed phenomenon in deep learning is the degradation problem: increasing the depth of a network leads to a decrease in performance on both test and training data.

Adaptive regularization for Lasso models in the context of non-stationary data streams

no code implementations28 Oct 2016 Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana

In this work consider the problem of learning $\ell_1$ regularized linear models in the context of streaming data.

Text-mining the NeuroSynth corpus using Deep Boltzmann Machines

no code implementations1 May 2016 Ricardo Pio Monti, Romy Lorenz, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana

Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function.

Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection

no code implementations7 Dec 2015 Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana

In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity.

Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces

no code implementations8 Feb 2015 Ricardo Pio Monti, Romy Lorenz, Christoforos Anagnostopoulos, Robert Leech, Giovanni Montana

Such studies have recently gained momentum and have been applied in a wide variety of settings; ranging from training of healthy subjects to self-regulate neuronal activity to being suggested as potential treatments for clinical populations.

Brain Computer Interface

Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series

no code implementations14 Oct 2013 Ricardo Pio Monti, Peter Hellyer, David Sharp, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana

We apply the SINGLE algorithm to functional MRI data from 24 healthy patients performing a choice-response task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task.

Time Series Time Series Analysis

Cannot find the paper you are looking for? You can Submit a new open access paper.