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  • 1
    Online Resource
    Online Resource
    Using Convolutional Neural Networks to Analyze Function Properties from Images
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2016
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 30, No. 1 ( 2016-03-05)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 30, No. 1 ( 2016-03-05)
    Abstract: We propose a system for determining properties of mathematical functions given an image of their graph representation. We demonstrate our approach for two-dimensional graphs (curves of single variable functions) and three-dimensional graphs (surfaces of two variable functions), studying the properties of convexity and symmetry. Our method uses a Convolutional Neural Network which classifies functions according to these properties, without using any hand-crafted features. We propose algorithms for randomly constructing functions with convexity or symmetry properties, and use the images generated by these algorithms to train our network. Our system achieves a high accuracy on this task, even for functions where humans find it difficult to determine the function's properties from its image.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v30i1.9843
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2016
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  • 2
    Online Resource
    Online Resource
    Agent Failures in All-Pay Auctions
    Institute of Electrical and Electronics Engineers (IEEE) ; 2017
    In:  IEEE Intelligent Systems Vol. 32, No. 1 ( 2017-1), p. 8-16
    Details
    In: IEEE Intelligent Systems, Institute of Electrical and Electronics Engineers (IEEE), Vol. 32, No. 1 ( 2017-1), p. 8-16
    Type of Medium: Online Resource
    ISSN: 1541-1672
    URL: Article
    DOI: 10.1109/MIS.2017.2
    RVK:
    SA 5515
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2017
    detail.hit.zdb_id: 1418637-8
    detail.hit.zdb_id: 2949329-8
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  • 3
    Online Resource
    Online Resource
    Knowing What to Ask: A Bayesian Active Learning Approach to the Surveying Problem
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1 ( 2017-02-12)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 31, No. 1 ( 2017-02-12)
    Abstract: We examine the surveying problem, where we attempt to predict how a target user is likely to respond to questions by iteratively querying that user, collaboratively based on the responses of a sample set of users. We focus on an active learning approach, where the next question we select to ask the user depends on their responses to the previous questions. We propose a method for solving the problem based on a Bayesian dimensionality reduction technique. We empirically evaluate our method, contrasting it to benchmark approaches based on augmented linear regression, and show that it achieves much better predictive performance, and is much more robust when there is missing data.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v31i1.10730
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
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  • 4
    Online Resource
    Online Resource
    Predicting Gaming Related Properties from Twitter Accounts
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2016
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 30, No. 1 ( 2016-03-05)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 30, No. 1 ( 2016-03-05)
    Abstract: We demonstrate a system for predicting gaming related properties from Twitter accounts. Our system predicts various traits of users based on the tweets publicly available in their profiles. Such inferred traits include degrees of tech-savviness and knowledge on computer games, actual gaming performance, preferred platform, degree of originality, humor and influence on others. Our system is based on machine learning models trained on crowd-sourced data. It allows people to select Twitter accounts of their fellow gamers, examine the trait predictions made by our system, and the main drivers of these predictions. We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v30i1.9842
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2016
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  • 5
    Online Resource
    Online Resource
    “Reverse Gerrymandering”: Manipulation in Multi-Group Decision Making
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2019
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, No. 01 ( 2019-07-17), p. 2069-2076
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 33, No. 01 ( 2019-07-17), p. 2069-2076
    Abstract: District-based manipulation, or gerrymandering, is usually taken to refer to agents who are in fixed location, and an external division is imposed upon them. However, in many real-world setting, there is an external, fixed division – an organizational chart of a company, or markets for a particular product. In these cases, agents may wish to move around (“reverse gerrymandering”), as each of them tries to maximize their influence across the company’s subunits, or resources are “working” to be allocated to areas where they will be most needed.In this paper we explore an iterative dynamic in this setting, finding that allowing this decentralized system results, in some particular cases, in a stable equilibrium, though in general, the setting may end up in a cycle. We further examine how this decentralized process affects the social welfare of the system.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v33i01.33012069
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2019
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  • 6
    Online Resource
    Online Resource
    Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2016
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 30, No. 1 ( 2016-03-05)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 30, No. 1 ( 2016-03-05)
    Abstract: We consider the task of predicting various traits of a person given an image of their face. We aim to estimate traits such as gender, ethnicity and age, as well as more subjective traits as the emotion a person expresses or whether they are humorous or attractive. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture, and corroborate that CNNs are promising for facial attribute prediction. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. We empirically analyze the performance of our proposed method, showing consistent improvement over the baselines across traits. We demonstrate our system on a sizeable Face Attributes Dataset (FAD), comprising of roughly 200,000 labels, for 10 most sought-after traits, for over 10,000 facial images.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v30i1.9844
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2016
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