b'Jordi Vitri\xc3\xa0'

research

∙ 09/05/2023

Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR data

Airborne LiDAR systems have the capability to capture the Earth's surfac...

0 Mariona Caros, et al. ∙

research

∙ 10/28/2022

Object Segmentation of Cluttered Airborne LiDAR Point Clouds

Airborne topographic LiDAR is an active remote sensing technology that e...

0 Mariona Caros, et al. ∙

research

∙ 01/30/2022

Deep Non-Crossing Quantiles through the Partial Derivative

Quantile Regression (QR) provides a way to approximate a single conditio...

0 Axel Brando, et al. ∙

research

∙ 07/09/2021

Algorithmic Causal Effect Identification with causaleffect

Our evolution as a species made a huge step forward when we understood t...

0 Martí Pedemonte, et al. ∙

research

∙ 03/05/2021

Graph Convolutional Embeddings for Recommender Systems

Modern recommender systems (RS) work by processing a number of signals t...

10 Paula Gómez Duran, et al. ∙

research

∙ 06/15/2020

Causal Inference with Deep Causal Graphs

Parametric causal modelling techniques rarely provide functionality for ...

0 Álvaro Parafita, et al. ∙

research

∙ 12/29/2019

Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability

Nowadays, the use of machine learning models is becoming a utility in ma...

4 José Mena, et al. ∙

research

∙ 12/10/2019

WCE Polyp Detection with Triplet based Embeddings

Wireless capsule endoscopy is a medical procedure used to visualize the ...

21 Pablo Laiz, et al. ∙

research

∙ 10/27/2019

Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

In regression tasks, aleatoric uncertainty is commonly addressed by cons...

0 Axel Brando, et al. ∙

research

∙ 09/19/2019

Explaining Visual Models by Causal Attribution

Model explanations based on pure observational data cannot compute the e...

0 Álvaro Parafita, et al. ∙

research

∙ 07/24/2018

Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series

Deep Learning is a consolidated, state-of-the-art Machine Learning tool ...

0 Axel Brando, et al. ∙

research

∙ 05/29/2018

Uncertainty Gated Network for Land Cover Segmentation

The production of thematic maps depicting land cover is one of the most ...

0 Guillem Pascual, et al. ∙

research

∙ 07/26/2016

Generic Feature Learning for Wireless Capsule Endoscopy Analysis

The interpretation and analysis of the wireless capsule endoscopy record...

0 Santi Seguí, et al. ∙

research

∙ 05/29/2015

Learning to count with deep object features

Learning to count is a learning strategy that has been recently proposed...

0 Santi Seguí, et al. ∙

Jordi Vitrià

Featured Co-authors

Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR data

Object Segmentation of Cluttered Airborne LiDAR Point Clouds

Deep Non-Crossing Quantiles through the Partial Derivative

Algorithmic Causal Effect Identification with causaleffect

Graph Convolutional Embeddings for Recommender Systems

Causal Inference with Deep Causal Graphs

Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability

WCE Polyp Detection with Triplet based Embeddings

Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

Explaining Visual Models by Causal Attribution

Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series

Uncertainty Gated Network for Land Cover Segmentation

Generic Feature Learning for Wireless Capsule Endoscopy Analysis

Learning to count with deep object features

Jordi Vitrià

Featured Co-authors

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