We address the problem of finding representative points of datasets by learning from multiple datasets and their ground-truth summaries. We develop a supervised subset selection framework, based on the facility location utility function, which learns to map datasets to their ground-truth representatives. To do so, we propose to learn representations of data so that the input of transformed data to the facility location recovers their ground-truth representatives. Given the NP-hardness of the utility function, we consider its convex relaxation based on sparse representation and investigate conditions under which the solution of the convex optimization recovers ground-truth representatives of each dataset. We design a loss function whose minimization over the parameters of the data representation network leads to satisfying the theoretical conditions, hence guaranteeing recovering ground-truth summaries. Given the non-convexity of the loss function, we develop an efficient learning scheme that alternates between representation learning by minimizing our proposed loss given the current assignments of points to ground-truth representatives and updating assignments given the current data representation. By experiments on the problem of learning key-steps (subactivities) of instructional videos, we show that our proposed framework improves the state-of-the-art supervised subset selection algorithms.

This paper proposes a sparse convex relation of the facility location utility function for subset selection, for the problem of recovering ground-truth representatives for datasets. This relaxation is used to develop a supervised learning approach for this problem, which involves a learning algorithm that alternatively updates three loss functions (Eq. The supervised facility learning approach described in this paper appears to be novel, and is described clearly. The experimental results are reasonably convincing overall. One weakness is that only one dataset is used, the Breakfast dataset.

The paper presents a supervised facility location based approach to subset selection, i.e., choosing a set of representative points from a new dataset. The paper considers a sparse convex relaxation of the problem and characterizes conditions for getting integral solutions. An alternating algorithm utilizing the integral solutions is presented for learning the subset mapping. Extensive experimental results are presented to illustrate the effectiveness of the proposed approach. The reviewers agree that the paper makes a novel contribution to an important problem and the paper is well written.

We address the problem of finding representative points of datasets by learning from multiple datasets and their ground-truth summaries. We develop a supervised subset selection framework, based on the facility location utility function, which learns to map datasets to their ground-truth representatives. To do so, we propose to learn representations of data so that the input of transformed data to the facility location recovers their ground-truth representatives. Given the NP-hardness of the utility function, we consider its convex relaxation based on sparse representation and investigate conditions under which the solution of the convex optimization recovers ground-truth representatives of each dataset. We design a loss function whose minimization over the parameters of the data representation network leads to satisfying the theoretical conditions, hence guaranteeing recovering ground-truth summaries.

The application of routing algorithms to real-world situations is a widely studied research topic. Despite this, routing algorithms and applications are usually developed for a general purpose, meaning that certain groups, such as ageing people, are often marginalized due to the broad approach of the designed algorithms. This situation may pose a problem in cities which are suffering a slow but progressive ageing of their populations. With this motivation in mind, this paper focuses on describing our implemented Age-Friendly Route Planner, whose goal is to improve the experience in the city for senior citizens. In order to measure the age-friendliness of a route, several variables have been deemed, such as the number of amenities along the route, the amount of comfortable elements found, or the avoidance of sloppy sections. In this paper, we describe one of the main features of the Age-Friendly Route Planner: the preference-based routes, and we also demonstrate how it can contribute to the creation of adapted friendly routes.

The most commonly used neural network models are not well suited to direct digital implementations because each node needs to per(cid:173) form a large number of operations between floating point values. Fortunately, the ability to learn from examples and to generalize is not restricted to networks ofthis type. Indeed, networks where each node implements a simple Boolean function (Boolean networks) can be designed in such a way as to exhibit similar properties. Two algorithms that generate Boolean networks from examples are pre(cid:173) sented. The results show that these algorithms generalize very well in a class of problems that accept compact Boolean network descriptions.

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Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others.

With the end of Moore's law approaching and Dennard scaling ending, the computing community is increasingly looking at new technologies to enable continued performance improvements. Neuromorphic computers are one such new computing technology. The term neuromorphic was coined by Carver Mead in the late 1980s1,2, and at that time primarily referred to mixed analogue–digital implementations of brain-inspired computing; however, as the field has continued to evolve and with the advent of large-scale funding opportunities for brain-inspired computing systems such as the DARPA Synapse project and the European Union's Human Brain Project, the term neuromorphic has come to encompass a wider variety of hardware implementations. We define neuromorphic computers as non-von Neumann computers whose structure and function are inspired by brains and that are composed of neurons and synapses. Von Neumann computers are composed of separate CPUs and memory units, where data and instructions are stored in the latter.

This breakthrough does not really require someone to feed the information to the computer or be their eyes so to say. Because this new technique allows machines to interpret and categorize whatever they see in images or videos. In other words, computers now have their own eyes. Therefore, they work independently with the ability to recognize whatever is around them. Here the model will predict only one label per image. What this means that no matter the input or the diversity in the image, the machine will assign only a single label.