Jesus Malo

Full Professor of Vision Science

University of Valencia

Spain

Research Interests

Explore related searches

Professors in Spain Professors in Neuroscience Neuroscience professors in Spain

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Jesus Malo is a Full Professor of Vision Science at Universitat de València in Spain. His research primarily focuses on image segmentation, perceptual image quality, and color discrimination, as evidenced by his recent publications. He explores topics such as divisive normalization in neural networks and the analytical results of functional connectivity in visual areas.

Articles (19)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (2)

Javier Vazquez-Corral

Universitat Autònoma de Barcelona (UAB)

SPAIN

View details

Greg Ver Steeg

Associate Professor

University of California, Riverside

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Jesus Malo

Research Interests

Explore related searches

Contact this professor

About

Articles (19)

Why Divisive Normalization works in image segmentation?

Dissecting the effectiveness of deep features as metric of perceptual image quality

Estimating Information Theoretic Measures via Multidimensional Gaussianization

Alignment of color discrimination in humans and image segmentation networks

Plaid masking explained with input-dependent dendritic nonlinearities

Cortical Divisive Normalization from Wilson–Cowan Neural Dynamics

Cortical Divisive Normalization from Wilson-Cowan Neural Dynamics

Functional connectivity via total correlation: Analytical results in visual areas

Functional connectivity via total correlation: Analytical results in visual areas

Neural networks with divisive normalization for image segmentation

Artificial psychophysics questions classical hue cancellation experiments

Functional Connectome of the Human Brain with Total Correlation

On the synthesis of visual illusions using deep generative models

Contrast sensitivity functions in autoencoders

Visual Information Fidelity with better Vision Models and better Mutual Information Estimates

Evidence for the intrinsically nonlinear nature of receptive fields in vision

Spatio-chromatic information available from different neural layers via Gaussianization

Color illusions also deceive CNNs for low-level vision tasks: Analysis and implications

Visual information flow in Wilson-Cowan networks.

Collaborators (2)

Javier Vazquez-Corral

Greg Ver Steeg

Make your next move.