Results 91 to 100 of about 36,247 (313)

Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications

Mathematics
This paper presents a probabilistic model for various machine learning (ML) applications. While deep learning (DL) has produced state-of-the-art results in many domains, DL models are complex and over-parameterized, which leads to high uncertainty about ...
Richard V. Field, Michael R. Smith, Ellery J. Wuest, Joe B. Ingram   +3 more
doaj   +1 more source

Explainability Is Not a Game

Communications of the ACM
When the decisions of ML models impact people, one should expect explanations to offer the strongest guarantees of rigor. However, the most popular XAI approaches offer none.
João Marques-Silva 0001, Xuanxiang Huang   +1 more
openaire   +2 more sources

Residual tail twisting in ascidian larvae is stabilized by asymmetric myofibrils that resist bilateral symmetry restoration

FEBS Letters, EarlyView.
Ascidian Ciona larvae initially show strong clockwise tail twisting, which is largely corrected during development. However, a small residual twist remains. This study shows that organized helical myofibrils in tail muscles mechanically stabilize this residual asymmetry, preventing complete restoration of bilateral symmetry and revealing how embryos ...
Yuki S. Kogure, Misaki K. Nishio, Satoru Okuda, Kohji Hotta   +3 more
wiley   +1 more source

Is Explainability Always Necessary? Discussion on Explainable AI

, 2022
The explainability of a model has been a topic of debate. Some research states explainability is unnecessary, and some ”white-box” models, such as regression models or decision trees, are inherently explainable.
Grigoryan, Gayane, Collins, Andrew J.
core   +1 more source

Interpretable Optimization: Why and How We Should Explain Optimization Models

Applied Sciences
Interpretability is widely recognized as essential in machine learning, yet optimization models remain largely opaque, limiting their adoption in high-stakes decision-making.
Sara Lumbreras, Pedro Ciller
doaj   +1 more source

Explainable ASP [PDF]

, 2019
Despite its proven relevance, ASP (answer set programming) suffers from a lack of transparency in its outputs. Much like other popular artificial intelligence systems such as deep learning, the results do not come with any explanation to support their derivation.
Dauphin, Jérémie, Satoh, Ken
openaire   +2 more sources

The human gut microbiome across the life course

FEBS Letters, EarlyView.
Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero, Basak Bahcivanci, Antonietta Hayhoe, Animesh Acharjee, Ezgi Özkurt   +4 more
wiley   +1 more source

Engineering Inter-agent Explainability in BDI Agents

Despite inter-agent explainability being recognised as a potential enabler of useful dynamics for communication and cooperation in belief-desire-intention (BDI) multi-agent systems, research on explainability has been mostly focused on targeting humans ...
Burattini, Samuele, Katharine Beaumont, Yan, Elena, Elena Yan, Beaumont, Katharine, Rem Collier, Samuele Burattini, Collier, Rem   +7 more
core   +1 more source

Prediction and explainability in AI: Striking a new balance?

Big Data & Society
The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models.
Aviad Raz, Bert Heinrichs, Netta Avnoon, Gil Eyal, Yael Inbar   +4 more
doaj   +1 more source

Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning

Biomimetics
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals.
Alka Jalan, Deepti Mishra, Marisha, Manjari Gupta   +3 more
doaj   +1 more source