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Recommender systems attempts to identify and recommend the most preferable item (product-service) to an individual user. These systems predict user interest in items based on related items, users, and the interactions between items and…

机器学习 · 计算机科学 2021-04-07 Atousa Zarindast , Jonathan Wood , Anuj Sharma

To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner.…

机器学习 · 计算机科学 2019-10-15 Judith Bütepage , Ali Ghadirzadeh , Özge Öztimur Karadag , Mårten Björkman , Danica Kragic

To learn object models for robotic manipulation, unsupervised methods cannot provide accurate object structural information and supervised methods require a large amount of manually labeled training samples, thus interactive object…

机器人学 · 计算机科学 2015-04-21 Kun Li , Max Q. -H. Meng

In a traditional convolutional layer, the learned filters stay fixed after training. In contrast, we introduce a new framework, the Dynamic Filter Network, where filters are generated dynamically conditioned on an input. We show that this…

机器学习 · 计算机科学 2016-06-07 Bert De Brabandere , Xu Jia , Tinne Tuytelaars , Luc Van Gool

To uncover the mechanisms underlying the collaborative production of knowledge, we investigate a very large online Question and Answer system that includes the question asking and answering activities of millions of users over five years.…

物理与社会 · 物理学 2015-11-25 Lingfei Wu , Marco A. Janssen

Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and…

信息检索 · 计算机科学 2023-04-04 Juan Pablo Equihua , Maged Ali , Henrik Nordmark , Berthold Lausen

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error…

人工智能 · 计算机科学 2011-08-30 Lilyana Mihalkova , Walaa Eldin Moustafa

Assembling lattices from discrete building blocks enables the composition of large, heterogeneous, and easily reconfigurable objects with desirable mass-to-stiffness ratios. This type of building system may also be referred to as a digital…

机器人学 · 计算机科学 2024-10-14 Dominik Fischer , Loi Do , Miana Smith , Jiří Zemánek

The Web today has millions of datasets, and the number of datasets continues to grow at a rapid pace. These datasets are not standalone entities; rather, they are intricately connected through complex relationships. Semantic relationships…

信息检索 · 计算机科学 2024-08-28 Kate Lin , Tarfah Alrashed , Natasha Noy

In order to successfully perform tasks specified by natural language instructions, an artificial agent operating in a visual world needs to map words, concepts, and actions from the instruction to visual elements in its environment. This…

计算机视觉与模式识别 · 计算机科学 2019-10-15 Soumik Dasgupta , Badri N. Patro , Vinay P. Namboodiri

Gestures are a natural form of communication between humans and can also be leveraged for human-robot interaction. This work presents a gesture-based user interface for object selection using pointing and click gestures. An experiment with…

机器人学 · 计算机科学 2026-04-08 Bijan Kavousian , Oliver Petrovic , Werner Herfs

Given a sequence of sets, where each set has a timestamp and contains an arbitrary number of elements, temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly focus…

机器学习 · 计算机科学 2023-08-29 Le Yu , Zihang Liu , Leilei Sun , Bowen Du , Chuanren Liu , Weifeng Lv

We describe a method for incrementally constructing belief networks. We have developed a network-construction language similar to a forward-chaining language using data dependencies, but with additional features for specifying…

人工智能 · 计算机科学 2013-04-05 Robert P. Goldman , Eugene Charniak

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of…

机器学习 · 计算机科学 2017-01-18 Lei Zheng , Vahid Noroozi , Philip S. Yu

It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on…

适应与自组织系统 · 物理学 2023-04-13 Rico Berner , Thilo Gross , Christian Kuehn , Jürgen Kurths , Serhiy Yanchuk

Recommender systems assist users in navigating complex information spaces and focus their attention on the content most relevant to their needs. Often these systems rely on user activity or descriptions of the content. Social annotation…

As humans, we have a remarkable capacity for reading the characteristics of objects only by observing how another person carries them. Indeed, how we perform our actions naturally embeds information on the item features. Collaborative…

We propose a procedure to generate dynamical networks with bursty, possibly repetitive and correlated temporal behaviors. Regarding any weighted directed graph as being composed of the accumulation of paths between its nodes, our…

物理与社会 · 物理学 2013-04-10 Alain Barrat , Bastien Fernandez , Kevin K Lin , Lai-Sang Young

An object-oriented approach to implementing artificial neural networks is introduced in this article. The networks obtained in this way are highly connected in that they admit edges between nodes in any layers of the network, and dynamic,…

机器学习 · 计算机科学 2023-02-20 Clint van Alten

Human-Object Interaction (HOI) Detection is an important problem to understand how humans interact with objects. In this paper, we explore Interactiveness Knowledge which indicates whether human and object interact with each other or not.…

计算机视觉与模式识别 · 计算机科学 2019-06-12 Yong-Lu Li , Siyuan Zhou , Xijie Huang , Liang Xu , Ze Ma , Hao-Shu Fang , Yan-Feng Wang , Cewu Lu
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