Ricardo Pio Monti

Orcid: 0000-0002-7823-4961

According to our database1, Ricardo Pio Monti authored at least 11 papers between 2015 and 2021.

Collaborative distances:
  • Dijkstra number2 of six.
  • Erdős number3 of five.

Timeline

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Links

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Bibliography

2021
Causal Autoregressive Flows.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Robust contrastive learning and nonlinear ICA in the presence of outliers.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Variational Autoencoders and Nonlinear ICA: A Unifying Framework.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018
A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data .
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Avoiding Degradation in Deep Feed-Forward Networks by Phasing Out Skip-Connections.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

2016
Text-mining the neurosynth corpus using deep boltzmann machines.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

Classifying HCP task-fMRI networks using heat kernels.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

2015
Graph Embeddings of Dynamic Functional Connectivity Reveal Discriminative Patterns of Task Engagement in HCP Data.
Proceedings of the 2015 International Workshop on Pattern Recognition in NeuroImaging, 2015


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