Isabelle Guyon

Demian Battaglia

Vincent Lemaire

Javier Orlandi

Bisakha Ray

Jordi Soriano

This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few. divThe book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives./divdivbr/divdivbr

Neural Connectomics Challenge

Alexander Statnikov

Berna Bakir Batu

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

Cause Effect Pairs in Machine Learning

Masoud Nikravesh

Lofti A. Zadeh

Steve Gunn

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Foundations and Applications

Feature Extraction

Le récit d'Isabelle Guyon évoque les terres promises qui jalonnent l'existence de son personnage principal, une jeune fille que nous suivons de l'adolescence à l'âge adulte. Tout au long de cette histoire, nous la voyons surmonter peu à peu les obstacles successifs auxquels elle est confrontée. Elle fait aussi l'expérience de la pauvreté et du dénuement. Les liens tissés avec les autres et la ténacité sont, comme dans les précédents textes du même auteur, les piliers essentiels sur lesquels elle s'appuie pour avancer.

Récit

Les Terres promises

Isabelle Guyon poursuit son exploration d'une vision optimiste de l'homme dans le Temps. Elle cherche notamment, avec Le Grain du Temps, les voies possibles pour réussir à nous échapper de la tyrannie du Temps et être capables, comme elle l'écrit, de "nous élancer, au-delà de notre réalité présente, au-delà de nous-mêmes, enfin libres."

Le Grain du Temps

Sergio Escalera

Vassilis Athitsos

This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.

Gesture Recognition

Yağmur Güçlütürk

Umut Güçlü

Hugo Jair Escalante

Marcel van Gerven

Xavier Baró

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Isabelle Guyon

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