Antonio Jose Rodríguez-Sánchez

Orcid: 0000-0002-3264-5060

According to our database1, Antonio Jose Rodríguez-Sánchez authored at least 57 papers between 2006 and 2023.

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

Timeline

Legend:

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Bibliography

2023
Continual learning from demonstration of robotics skills.
Robotics Auton. Syst., 2023

Scalable and Efficient Continual Learning from Demonstration via Hypernetwork-generated Stable Dynamics Model.
CoRR, 2023

2022
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization.
Trans. Mach. Learn. Res., 2022

Momentum Capsule Networks.
Trans. Mach. Learn. Res., 2022

Greedy-layer pruning: Speeding up transformer models for natural language processing.
Pattern Recognit. Lett., 2022

Continual Learning from Demonstration of Robotic Skills.
CoRR, 2022

Evaluating Attention in Convolutional Neural Networks for Blended Images.
Proceedings of the 5th IEEE International Conference on Image Processing Applications and Systems, 2022

Improving 3D Point Cloud Reconstruction with Dynamic Tree-Structured Capsules.
Proceedings of the 5th IEEE International Conference on Image Processing Applications and Systems, 2022

Deep Learning for Fast Segmentation of E-waste Devices' Inner Parts in a Recycling Scenario.
Proceedings of the Pattern Recognition and Artificial Intelligence, 2022

Affordance detection with Dynamic-Tree Capsule Networks.
Proceedings of the 21st IEEE-RAS International Conference on Humanoid Robots, 2022

2021
conflicting_bundle.py - A python module to identify problematic layers in deep neural networks.
Softw. Impacts, 2021

Limitation of capsule networks.
Pattern Recognit. Lett., 2021

Arguments for the unsuitability of convolutional neural networks for non-local tasks.
Neural Networks, 2021

Greedy Layer Pruning: Decreasing Inference Time of Transformer Models.
CoRR, 2021

Auto-tuning of Deep Neural Networks by Conflicting Layer Removal.
CoRR, 2021

Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Training Deep Capsule Networks with Residual Connections.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

2020
A robust contour detection operator with combined push-pull inhibition and surround suppression.
Inf. Sci., 2020

Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates.
Frontiers Robotics AI, 2020

Evaluating the Progress of Deep Learning for Visual Relational Concepts.
CoRR, 2020

Plant Growth Prediction through Intelligent Embedded Sensing.
Proceedings of the 29th IEEE International Symposium on Industrial Electronics, 2020

Deconvolution of Image Sequences with a Learning FFT-based Approach.
Proceedings of the 29th IEEE International Symposium on Industrial Electronics, 2020

2019
Limitations of routing-by-agreement based capsule networks.
CoRR, 2019

Towards affordance detection for robot manipulation using affordance for parts and parts for affordance.
Auton. Robots, 2019

Capsule Networks for Attention Under Occlusion.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

Evaluating CNNs on the Gestalt Principle of Closure.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, 2019

2018
Exercising Affordances of Objects: A Part-Based Approach.
IEEE Robotics Autom. Lett., 2018

Training Deep Capsule Networks.
CoRR, 2018

ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

2017
Guided Labeling using Convolutional Neural Networks.
CoRR, 2017

Can Affordances Guide Object Decomposition into Semantically Meaningful Parts?
Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, 2017

A deep learning approach for detecting and correcting highlights in endoscopic images.
Proceedings of the Seventh International Conference on Image Processing Theory, 2017

Evaluation of Deep Learning on an Abstract Image Classification Dataset.
Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

2016
Can computer vision problems benefit from structured hierarchical classification?
Mach. Vis. Appl., 2016

Monocular obstacle avoidance for blind people using probabilistic focus of expansion estimation.
Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, 2016

25 Years of CNNs: Can We Compare to Human Abstraction Capabilities?
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Learning V4 Curvature Cell Populations from Sparse Endstopped Cells.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Kronecker Decomposition for Image Classification.
Proceedings of the Experimental IR Meets Multilinguality, Multimodality, and Interaction, 2016

2015
Beyond Simple and Complex Neurons: Towards Intermediate-level Representations of Shapes and Objects.
Künstliche Intell., 2015

Diversity priors for learning early visual features.
Frontiers Comput. Neurosci., 2015

Editorial: Hierarchical Object Representations in the Visual Cortex and Computer Vision.
Frontiers Comput. Neurosci., 2015

SCurV: A 3D descriptor for object classification.
Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015

CPS: 3D Compositional Part Segmentation through Grasping.
Proceedings of the 12th Conference on Computer and Robot Vision, 2015

IIS at ImageCLEF 2015: Multi-label Classification Task.
Proceedings of the Working Notes of CLEF 2015, 2015

Can Computer Vision Problems Benefit from Structured Hierarchical Classification?
Proceedings of the Computer Analysis of Images and Patterns, 2015

Learning Abstract Classes using Deep Learning.
Proceedings of the BICT 2015, 2015

2014
Towards Sparsity and Selectivity: Bayesian Learning of Restricted Boltzmann Machine for Early Visual Features.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?
IEEE Trans. Pattern Anal. Mach. Intell., 2013

ÖAGM/AAPR 2013 - The 37th Annual Workshop of the Austrian Association for Pattern Recognition
CoRR, 2013

Proceedings of the 37th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM/AAPR), 2013
CoRR, 2013

Models of the Visual Cortex for Object Representation: Learning and Wired Approaches.
Proceedings of the Brain-Inspired Computing - International Workshop, 2013

Detecting, Representing and Attending to Visual Shape.
Proceedings of the Shape Perception in Human and Computer Vision, 2013

2011
The importance of intermediate representations for the modeling of 2D shape detection: Endstopping and curvature tuned computations.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2008
Visual Feature Binding within the Selective Tuning Attention Framework.
Int. J. Pattern Recognit. Artif. Intell., 2008

2007
Attention and Visual Search.
Int. J. Neural Syst., 2007

Different Binding Strategies for the Different Stages of Visual Recognition.
Proceedings of the Advances in Brain, 2007

2006
Feature Conjunctions in Visual Search.
Proceedings of the Artificial Neural Networks, 2006


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