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Journal ArticleDOI

Predicting Human Decision-Making: From Prediction to Action

TLDR
The task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures is explored—from purely conflicting interaction settings to fully cooperative interaction settings.
Abstract
Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent a...

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Citations
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Journal ArticleDOI

Knowledge Distillation: A Survey

TL;DR: A comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison and applications can be found in this paper.
Journal ArticleDOI

Knowledge Distillation: A Survey

TL;DR: A comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, distillation algorithms and applications is provided.
Journal ArticleDOI

SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search

TL;DR: In this article, a framework for scene classification network architecture search based on multi-objective neural evolution (SceneNet) is proposed, and the effectiveness of SceneNet is demonstrated by experimental comparisons with several deep neural networks designed by human experts.
Journal ArticleDOI

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review.

TL;DR: In this paper, the authors provide an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. But, the field of deep learning is relatively new and need further research for its full utilization.
Journal ArticleDOI

Using large-scale experiments and machine learning to discover theories of human decision-making

TL;DR: This article used large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories for predicting and understanding how people make decisions, and showed how progress toward this goal can be accelerated by using large datasets.
References
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Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI

A Survey on Transfer Learning

TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Journal ArticleDOI

SMOTE: synthetic minority over-sampling technique

TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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