34 citations · 202 across the 26 of their papers we have counts for
30 papers
Task-Oriented Rank Adaptation for Continual Learning in Text Classification
Rey Sanchez Lopez, Eduardo Morales Manzanares, Hugo Jair Escalante
Continual learning (CL) in text classification faces two critical challenges: catastrophic forgetting and negative transfer across sequential tasks. Parameter-Efficient Fine-Tuning…
Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search
Cosijopii Garcia-Garcia, Alicia Morales-Reyes, Hugo Jair Escalante
We propose a novel approach for the challenge of designing less complex yet highly effective convolutional neural networks (CNNs) through the use of cartesian genetic programming (…
Comparison of classifiers in challenge scheme
Sergio Nava-Muñoz, Mario Graff Guerrero, Hugo Jair Escalante
In recent decades, challenges have become very popular in scientific research as these are crowdsourcing schemes. In particular, challenges are essential for developing machine lea…
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
Dong Wang, Jia Guo, Qiqi Shao +9
Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of exi…
Surveillance Face Presentation Attack Detection Challenge
Hao Fang, Ajian Liu, Jun Wan +3
Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios…
Predicting students' performance in online courses using multiple data sources
Mélina Verger, Hugo Jair Escalante
Data-driven decision making is serving and transforming education. We approached the problem of predicting students' performance by using multiple data sources which came from onli…
Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition
Fernanda Hernández-Luquin, Hugo Jair Escalante
Emotion recognition (ER) from facial images is one of the landmark tasks in affective computing with major developments in the last decade. Initial efforts on ER relied on handcraf…
3D High-Fidelity Mask Face Presentation Attack Detection Challenge
Ajian Liu, Chenxu Zhao, Zitong Yu +8
The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale Hi…
ChaLearn Looking at People: Inpainting and Denoising challenges
Sergio Escalera, Marti Soler, Stephane Ayache +6
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the pro…
Causal Structure Learning: a Bayesian approach based on random graphs
Mauricio Gonzalez-Soto, Ivan R. Feliciano-Avelino, L. Enrique Sucar +1
A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existe…
Automated Machine Learning -- a brief review at the end of the early years
Hugo Jair Escalante
Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the cont…
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
Ajian Liu, Xuan Li, Jun Wan +12
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the exc…
Causal Games and Causal Nash Equilibrium
Mauricio Gonzalez-Soto, Luis E. Sucar, Hugo J. Escalante
Classical results of Decision Theory, and its extension to a multi-agent setting: Game Theory, operate only at the associative level of information; this is, classical decision mak…
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing
Shifeng Zhang, Ajian Liu, Jun Wan +5
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmar…
Towards AutoML in the presence of Drift: first results
Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales +5
Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn. However, so far the…
Meta-learning of textual representations
Jorge Madrid, Hugo Jair Escalante, Eduardo Morales
Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas thes…
AutoML @ NeurIPS 2018 challenge: Design and Results
Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon +5
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participa…
A Guiding Principle for Causal Decision Problems
M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
We define a Causal Decision Problem as a Decision Problem where the available actions, the family of uncertain events and the set of outcomes are related through the variables of a…
Playing against Nature: causal discovery for decision making under uncertainty
M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
We consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to…
A visual approach for age and gender identification on Twitter
Miguel A. Alvarez-Carmona, Luis Pellegrin, Manuel Montes-y-Gómez +5
The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gai…
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis
Julio C. S. Jacques Junior, Yağmur Güçlütürk, Marc Pérez +8
Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive resea…
Towards Deep Representation Learning with Genetic Programming
Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante
Genetic Programming (GP) is an evolutionary algorithm commonly used for machine learning tasks. In this paper we present a method that allows GP to transform the representation of…
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah +14
Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analy…
Learning Functional Causal Models with Generative Neural Networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou +3
We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural networks to l…