Noe Hernandez

@iimas.unam.mx

Departamento de Ciencias de la Computación
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México

Noe Hernandez


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https://researchid.co/nohernan

EDUCATION

2026, PhD, Universidad Nacional Autónoma de México
2014, MSc, Universidad Nacional Autónoma de México
2012, Bachelor's degree, Universidad Nacional Autónoma de México

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Multidisciplinary
7

Scopus Publications

56

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Remembering CIFAR-10 images with the entropic associative memory
    Noé Hernández, Rafael Morales, Luis A. Pineda
    Pattern Recognition, 2026
    • The Entropic Associative Memory (EAM) has a satisfactory performance using CIFAR-10. • The compression ratio of the memory storing 20% of CIFAR-10 is 1,125. • EAM rejects cues covered by a large patch, just as natural memory would do it. • The retrieved images are remembered, associated or imaged objects, but also noise. • The new quantization improves the system’s performance with respect to previous works. The entropic associative memory (EAM) is a computational model of natural memory incorporating some of its putative properties of being associative, distributed, declarative, abstractive, and constructive. Previous experiments satisfactorily tested the model on structured, homogeneous, and conventional data: images of manuscript digits and letters, images of clothing, and phone representations. In this work, we show that EAM appropriately stores, recognizes, and retrieves diverse and complex images of animals and vehicles in the CIFAR-10 dataset. The memory system generates meaningful retrieval association chains for such complex images. The retrieved objects can be seen as proper memories, associated recollections, or products of imagination, but also as noise. Furthermore, EAM is able to reject patched cues not recognizable by people, providing additional support for the similarity between EAM and natural memory; in contrast, autoencoders reproduce the images with the patch, and other memory models retrieve the original input cues completely.
  • Entropic associative memory for manuscript symbols
    Rafael Morales, Noé Hernández, Ricardo Cruz, Victor D. Cruz, Luis A. Pineda
    Plos One, 2022
    Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.
  • Deliberative and conceptual inference in service robots
    Luis A. Pineda, Noé Hernández, Arturo Rodríguez, Ricardo Cruz, Gibrán Fuentes
    Applied Sciences Switzerland, 2021
    Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios, the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here, we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making, and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot’s knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem-space that is explored through heuristic search, and the latter is based on conceptual knowledge, including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot’s communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.
  • Practical non-monotonic knowledge-base system for un-regimented domains: A Case-study in digital humanities
    Luis A. Pineda, Noé Hernández, Iván Torres, Gibrán Fuentes, Nydia Pineda De Avila
    Information Processing and Management, 2020
  • Reasoning with preferences in service robots
    Ivan Torres, Noé Hernández, Arturo Rodríguez, Gibrán Fuentes, Luis A. Pineda
    Journal of Intelligent and Fuzzy Systems, 2019
    Service Robots should be able to reason about preferences when assisting people in common daily tasks. This functionality is useful, for instance, to respond to action directives that conflict with the user’s interest or wellbeing or when commands are underspecified. Preferences are defeasible knowledge as they can change with time or context, and should be stored in a non-monotonic knowledge-base system, capable of expressing incomplete knowledge, updating defaults and exceptions dynamically, and handling multiple extensions. In this paper a knowledge-base system with such an expressive power is presented. Non-monotonicity is handled using a generalization of the Principle of Specificity, which states that in case of knowledge conflict the most specific proposition should be preferred. Reasoning about preferences is used on demand through conversational protocols that are generic and domain independent. We describe the general principles underlying such protocols and their implementation through the SitLog programming language. We also show a demonstration scenario in which the robot Golem-III assists human users using such protocols and preferences stored in its non-monotonic knowledege-base service.
  • Opportunistic inference and emotion in service robots
    Luis A. Pineda, Arturo Rodríguez, Gibrán Fuentes, Noé Hernández, Mauricio Reyes, Caleb Rascón, Ricardo Cruz, Ivette Vélez, Hernando Ortega
    Journal of Intelligent and Fuzzy Systems, 2018
    In this paper a strategy for incorporating a flexible and reliable high-level inference module in service robots is presented. This module is a part of the robot’s cognitive architecture which coordinates perception, inference and action within the robot’s communication and interaction cycle. The present approach relies on an explicit representation of the structure of the task performed by the robot. There are three kinds of inferences that the robot can use opportunistically along the task: (1) diagnosis, (2) decision making and (3) planning; each kind can be used in specific situations of the task structure or performed in arbitrary situations with recovery purposes when there is an interaction failure. In this latter case the three kinds of inference are performed sequentially in what we call the daily-life inference cycle . The inference cycle allows the incorporation of basic emotions in the robot’s behavior. A case study incorporating these functionalities in the robot Golem-III is presented. The paper is concluded with a reflection on the opportunistic use of inference schemes to support flexible and robust behavior, including the expression of emotions, in service robots.
  • Marimba: A tool for verifying properties of hidden markov models
    Noé Hernández, Kerstin Eder, Evgeni Magid, Jesús Savage, David A. Rosenblueth
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015

RECENT SCHOLAR PUBLICATIONS

  • Remembering CIFAR-10 images with the entropic associative memory
    N Hernández, R Morales, LA Pineda
    Pattern Recognition, 112639 , 2025
    2025.0
    Citations: 2
  • Entropic associative memory for real world images
    N Hernández, R Morales, LA Pineda
    arXiv preprint arXiv:2405.12500 , 2024
    2024.0
  • Entropic associative memory for manuscript symbols
    R Morales, N Hernández, R Cruz, VD Cruz, LA Pineda
    Plos one 17 (8), e0272386 , 2022
    2022.0
    Citations: 11
  • Deliberative and conceptual inference in service robots
    LA Pineda, N Hernández, A Rodríguez, R Cruz, G Fuentes
    Applied Sciences 11 (4), 1523 , 2021
    2021.0
    Citations: 4
  • Practical non-monotonic knowledge-base system for un-regimented domains: A Case-study in digital humanities
    LA Pineda, N Hernández, I Torres, G Fuentes, NP De Avila
    Information Processing & Management 57 (3), 102214 , 2020
    2020.0
    Citations: 8
  • Reasoning with preferences in service robots
    I Torres, N Hernández, A Rodríguez, G Fuentes, LA Pineda
    Journal of Intelligent & Fuzzy Systems 36 (5), 5105-5114 , 2019
    2019.0
    Citations: 15
  • Opportunistic inference and emotion in service robots
    LA Pineda, A Rodríguez, G Fuentes, N Hernández, M Reyes, C Rascón, ...
    Journal of Intelligent & Fuzzy Systems 34 (5), 3301-3311 , 2018
    2018.0
    Citations: 4
  • The Golem Team
    LA Pineda, C Rascon, G Fuentes, A Rodrıguez, H Ortega, M Reyes, ...
    RoboCup@ Home 2017 , 2017
    2017.0
    Citations: 11
  • Marimba: a tool for verifying properties of hidden markov models
    N Hernández, K Eder, E Magid, J Savage, DA Rosenblueth
    International Symposium on Automated Technology for Verification and … , 2015
    2015.0
    Citations: 1
  • Marimba: A tool for verifying properties of hidden markov models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture …
    N Hernández, K Eder, E Magid, J Savage, DA Rosenblueth
  • The Golem Team, RoboCup@ Home 2020
    LA Pineda, G Fuentes, A Rodrıguez, H Ortega, M Reyes, N Hernández

MOST CITED SCHOLAR PUBLICATIONS

  • Reasoning with preferences in service robots
    I Torres, N Hernández, A Rodríguez, G Fuentes, LA Pineda
    Journal of Intelligent & Fuzzy Systems 36 (5), 5105-5114 , 2019
    2019.0
    Citations: 15
  • Entropic associative memory for manuscript symbols
    R Morales, N Hernández, R Cruz, VD Cruz, LA Pineda
    Plos one 17 (8), e0272386 , 2022
    2022.0
    Citations: 11
  • The Golem Team
    LA Pineda, C Rascon, G Fuentes, A Rodrıguez, H Ortega, M Reyes, ...
    RoboCup@ Home 2017 , 2017
    2017.0
    Citations: 11
  • Practical non-monotonic knowledge-base system for un-regimented domains: A Case-study in digital humanities
    LA Pineda, N Hernández, I Torres, G Fuentes, NP De Avila
    Information Processing & Management 57 (3), 102214 , 2020
    2020.0
    Citations: 8
  • Deliberative and conceptual inference in service robots
    LA Pineda, N Hernández, A Rodríguez, R Cruz, G Fuentes
    Applied Sciences 11 (4), 1523 , 2021
    2021.0
    Citations: 4
  • Opportunistic inference and emotion in service robots
    LA Pineda, A Rodríguez, G Fuentes, N Hernández, M Reyes, C Rascón, ...
    Journal of Intelligent & Fuzzy Systems 34 (5), 3301-3311 , 2018
    2018.0
    Citations: 4
  • Remembering CIFAR-10 images with the entropic associative memory
    N Hernández, R Morales, LA Pineda
    Pattern Recognition, 112639 , 2025
    2025.0
    Citations: 2
  • Marimba: a tool for verifying properties of hidden markov models
    N Hernández, K Eder, E Magid, J Savage, DA Rosenblueth
    International Symposium on Automated Technology for Verification and … , 2015
    2015.0
    Citations: 1
  • Entropic associative memory for real world images
    N Hernández, R Morales, LA Pineda
    arXiv preprint arXiv:2405.12500 , 2024
    2024.0
  • Marimba: A tool for verifying properties of hidden markov models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture …
    N Hernández, K Eder, E Magid, J Savage, DA Rosenblueth
  • The Golem Team, RoboCup@ Home 2020
    LA Pineda, G Fuentes, A Rodrıguez, H Ortega, M Reyes, N Hernández