Matias Valdenegro-Toro

Orcid: 0000-0001-5793-9498

According to our database1, Matias Valdenegro-Toro authored at least 54 papers between 2016 and 2024.

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

Timeline

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Bibliography

2024
Uncertainty Quantification for Gradient-based Explanations in Neural Networks.
CoRR, 2024

Sanity Checks for Explanation Uncertainty.
CoRR, 2024

Transferring BCI models from calibration to control: Observing shifts in EEG features.
CoRR, 2024

Uncertainty Quantification for cross-subject Motor Imagery classification.
CoRR, 2024

Mapping Transformer Leveraged Embeddings for Cross-Lingual Document Representation.
CoRR, 2024

2023
Uncertainty Quantification in Machine Learning for Biosignal Applications - A Review.
CoRR, 2023

ChatGPT Prompting Cannot Estimate Predictive Uncertainty in High-Resource Languages.
CoRR, 2023

Sanity Checks for Saliency Methods Explaining Object Detectors.
Proceedings of the Explainable Artificial Intelligence, 2023

DExT: Detector Explanation Toolkit.
Proceedings of the Explainable Artificial Intelligence, 2023

Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

The VVAD-LRS3 Dataset for Visual Voice Activity Detection.
Proceedings of the 18th International Joint Conference on Computer Vision, 2023

Difficulty Estimation with Action Scores for Computer Vision Tasks.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
DExT: Detector Explanation Toolkit.
CoRR, 2022

Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models.
CoRR, 2022

A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation.
CoRR, 2022

Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression.
CoRR, 2022

Machine Learning Students Overfit to Overfitting.
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022

A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Self-supervised Learning for Sonar Image Classification.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021
Feature Disentanglement of Robot Trajectories.
CoRR, 2021

Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning.
CoRR, 2021

Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings.
CoRR, 2021

Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study.
CoRR, 2021

Pre-trained Models for Sonar Images.
CoRR, 2021

Forward-Looking Sonar Patch Matching: Modern CNNs, Ensembling, and Uncertainty.
CoRR, 2021

Teaching Uncertainty Quantification in Machine Learning through Use Cases.
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, 2021

The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

I Find Your Lack of Uncertainty in Computer Vision Disturbing.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
Improving predictive uncertainty estimation using Dropout-Hamiltonian Monte Carlo.
Soft Comput., 2020

Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?
CoRR, 2020

Unsupervised Difficulty Estimation with Action Scores.
CoRR, 2020

Automatic Detection and Classification of Tick-borne Skin Lesions using Deep Learning.
CoRR, 2020

Black-Box Optimization of Object Detector Scales.
CoRR, 2020

Perception for Autonomous Systems (PAZ).
CoRR, 2020

Can Reinforcement Learning for Continuous Control Generalize Across Physics Engines?
CoRR, 2020

Know Where To Drop Your Weights: Towards Faster Uncertainty Estimation.
CoRR, 2020

Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!
CoRR, 2020

Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds.
CoRR, 2020

2019
Deep neural networks for marine debris detection in sonar images.
PhD thesis, 2019

Results from the Robocademy ITN: Autonomy, Disturbance Rejection and Perception for Advanced Marine Robotics.
CoRR, 2019

Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification.
CoRR, 2019

Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition.
CoRR, 2019

Deep Neural Networks for Marine Debris Detection in Sonar Images.
CoRR, 2019

Implementing Noise with Hash functions for Graphics Processing Units.
CoRR, 2019

Real-time Convolutional Neural Networks for emotion and gender classification.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Learning Objectness from Sonar Images for Class-Independent Object Detection.
Proceedings of the 2019 European Conference on Mobile Robots, 2019

2018
Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks.
CoRR, 2018

Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo.
CoRR, 2018

2017
Image Captioning and Classification of Dangerous Situations.
CoRR, 2017

Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition.
CoRR, 2017

Object classification with convolution neural network based on the time-frequency representation of their echo.
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

Real-time convolutional networks for sonar image classification in low-power embedded systems.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Improving sonar image patch matching via deep learning.
Proceedings of the 2017 European Conference on Mobile Robots, 2017

2016
Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks.
Proceedings of the Artificial Neural Networks in Pattern Recognition, 2016


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