DOCSLIB.ORG

Automatic Opioid User Detection from Twitter

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Automatic Opioid User Detection from Twitter Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) Automatic Opioid User Detection From Twitter: Transductive Ensemble Built On Different Meta-graph Based Similarities Over Heterogeneous Information Network Yujie Fan, Yiming Zhang, Yanfang Ye ∗, Xin Li Department of Computer Science and Electrical Engineering, West Virginia University, WV, USA fyf0004,[email protected], fyanfang.ye,[email protected] Abstract 13,000 people died from heroin overdose, both reflecting sig- nificant increase from 2002 [NIDA, 2017]. Opioid addiction Opioid (e.g., heroin and morphine) addiction has is a chronic mental illness that requires long-term treatment become one of the largest and deadliest epidemics and care [McLellan et al., 2000]. Although Medication As- in the United States. To combat such deadly epi- sisted Treatment (MAT) using methadone or buprenorphine demic, in this paper, we propose a novel frame- has been proven to provide best outcomes for opioid addiction work named HinOPU to automatically detect opi- recovery, stigma (i.e., bias) associated with MAT has limited oid users from Twitter, which will assist in sharp- its utilization [Saloner and Karthikeyan, 2015]. Therefore, ening our understanding toward the behavioral pro- there is an urgent need for novel tools and methodologies to cess of opioid addiction and treatment. In HinOPU, gain new insights into the behavioral processes of opioid ad- to model the users and the posted tweets as well diction and treatment. as their rich relationships, we introduce structured In recent years, the role of social media in biomedical heterogeneous information network (HIN) for rep- knowledge mining has become more and more important resentation. Afterwards, we use meta-graph based [Hawn, 2009; Alkhateeb et al., 2011]. Due to the increas- approach to characterize the semantic relatedness ing use of the Internet, never-ending growth of data is gener- over users; we then formulate different similari- ated from the social media which offers opportunities for the ties over users based on different meta-graphs on users to freely share opinions and experiences in online com- HIN. To reduce the cost of acquiring labeled sam- munities. For example, Twitter, as one of the most popular ples for supervised learning, we propose a trans- social media platforms, has averaged at 328 million monthly ductive classification method to build the base clas- active users as of the second quarter of 2017 [Neha, 2017]. sifiers based on different similarities formulated by Many Twitter users are willing to share their experiences of different meta-graphs. Then, to further improve the using opioids (e.g., “It started with anorexia as I was 11, with detection accuracy, we construct an ensemble to 13 I was on heroin. At every time I leave heroin I fall into combine different predictions from different base anorexia”), and perceptions toward MAT (e.g., “I stopped classifiers for opioid user detection. Comprehen- with heroin bc I’m having the methadone, that is the most sive experiments on real sample collections from effective treatment.”). Thus, the data from social media may Twitter are conducted to validate the effectiveness contribute information beyond the knowledge of domain pro- of HinOPU in opioid user detection by compar- fessionals (e.g., psychiatrists and epidemics researchers) and isons with other alternate methods. could assist in sharpening our understanding toward the be- havioral process of opioid addiction and treatment. 1 Introduction To combat the opioid addiction epidemic and promote the practice of MAT, in this paper, we propose a novel frame- Opioids are a class of drugs including the illegal drug heroin work named HinOPU, a transductive ensemble classification and powerful pain relievers by legal prescription, such as model built on different meta-graph based similarities over morphine and oxycodone [NIDA, 2014]. Opioid addiction heterogeneous information network (HIN), to automatically has become one of the largest and deadliest epidemics in the detect opioid users from Twitter. To model the users and the U.S. [NIDA, 2017], which has also turned into a serious na- posted tweets as well as their rich semantic relationships, in tional crisis that affects public health as well as social and HinOPU, we first introduce a HIN [Sun et al., 2011] for rep- economic welfare. There was a skyrocketing increase of opi- resentation. To capture the complex relationship (e.g. two oid related death in the past decade: according to Centers for users are relevant if they have posted tweets that discussed Disease Control and Prevention (CDC), in 2015, more than the same topic, mentioned same people, and also reposted 32,000 Americans died from opioid drugs overdoses and over same tweets from others), we use meta-graphs [Zhao et al., 2017] to incorporate higher-level semantics to build up relat- ∗Corresponding author. edness over users. Different similarities over users can then 3357 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) Figure 1: System architecture of HinOPU. be formulated by different meta-graphs. As obtaining the la- supervised learning, transductive classification method is beled data (either opioid users or not) from Twitter is both proposed to build the base classifiers; then an ensemble time-consuming and cost-expensive, to reduce the cost of ac- approach is presented to combine different results from the quiring labeled samples for supervised learning, we propose a base classifiers to make final predictions. (See Section 3.3.) transductive classification method to build the base classifiers based on different similarities formulated by different meta- 3 Proposed Method graphs; to further improve the detection accuracy, we then In this section, we introduce the detailed approaches of how present an ensemble approach to combine different predic- we represent Twitter users, and how we solve the problem of tions from different base classifiers for opioid user detection. opioid user detection based on this representation. 2 System Architecture 3.1 HIN Construction The system architecture of HinOPU is shown in Figure.1, To detect whether a user is an opioid user or not from Twit- which consists of the following four major components: ter, besides the users’ posted tweets, the following kinds of relationships are also considered: • Data collector and preprocessor. We first develop web crawling tools to collect opioid-related tweets (i.e., the • R1: To describe the relation of a user and his/her posted tweets include opioid names or their common street names) tweet, we build the user-post-tweet matrix P where each as well as users’ profiles from Twitter. For privacy protec- element pi;j 2 f0; 1g denotes if user i posts tweet j. tion, UserID is used to anonymize each user. For the col- • R2: To denote the relation that a user appreciates a tweet, lected tweets, the preprocessor will remove all the links, we generate the user-like-tweet matrix L where each ele- punctuations, stopwords, and conduct lemmatization using ment li;j 2 f0; 1g means if user i likes tweet j. Stanford CoreNLP [Manning et al., 2014]; as street names can be used to imply opioids (e.g., smack is used to refer • R3: Two users can follow each other in Twitter. To rep- heroin) and may also have different meanings when given resent such kind of user-user relationship, we generate different contexts, we will then perform word sense dis- the user-follow-user matrix F where each element fi;j 2 ambiguation (WSD) [Zhong and Ng, 2010] to decide if a f0; 1g denotes whether user i and user j follow each other. tweet containing the street name is an opioid-related tweet. • R4: If a tweet includes symbol @ followed by a user name, • Feature extractor and HIN constructor. A bag-of-words it means that the user is mentioned and talked publicly in feature vector is extracted to represent each user’s posted the tweet. To describe such tweet-user relationship, we tweet(s); then the relationships among users, tweets and build the tweet-mention-user matrix A where each ele- topics will be further analyzed, such as, i) user-post-tweet, ment ai;j 2 f0; 1g indicates if tweet i mentions user j. ii) user-like-tweet, iii) user-follow-user, iv) tweet-mention- • R5: A tweet can be a repost of another tweet. To represent user, v) tweet-RT-tweet, and vi) tweet-contain-topic. Based such relationship between two tweets, we build the tweet- on these extracted features, a structural HIN will be con- RT-tweet matrix X where element xi;j 2 f0; 1g denotes structed. (See Section 3.1.) whether tweet i or tweet j is a repost of the other. • Meta-graph based similarity builder. In this module, dif- • R6: To represent the relation that a tweet contains a specific ferent meta-graphs are first built from HIN to capture the topic, we generate the tweet-contain-topic matrix C whose relatedness between users. Then, we integrate similarity element ci;j 2 f0; 1g indicates whether tweet i contains of users’ posted tweets and relatedness depicted by each topic j. In our case, we use Latent Dirichlet Allocation meta-graph to formulate a set of similarity measures over [Blei et al., 2003] for topic extraction from posted tweets. users. (See Section 3.2.) In order to depict users, tweets, topics and the rich re- • Transductive ensemble. Based on different similarities lationships among them (i.e., user-user, user-tweet, tweet- formulated by different meta-graphs in the previous mod- tweet, tweet-topic relations), it is important to model them in ule, to reduce the cost of acquiring labeled samples for a proper way so that different kinds of relations can be better 3358 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) and easier handled.
Load more

Recommended publications
  • Risk Bounds for Classification and Regression Rules That Interpolate
    Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate Mikhail Belkin Daniel Hsu Partha P. Mitra The Ohio State University Columbia University Cold Spring Harbor Laboratory Abstract Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phe- nomenon of strong generalization performance for “overfitted” / interpolated clas- sifiers appears to be ubiquitous in high-dimensional data, having been observed in deep networks, kernel machines, boosting and random forests. Their performance is consistently robust even when the data contain large amounts of label noise. Very little theory is available to explain these observations. The vast majority of theoretical analyses of generalization allows for interpolation only when there is little or no label noise. This paper takes a step toward a theoretical foundation for interpolated classifiers by analyzing local interpolating schemes, including geometric simplicial interpolation algorithm and singularly weighted k-nearest neighbor schemes. Consistency or near-consistency is proved for these schemes in classification and regression problems. Moreover, the nearest neighbor schemes exhibit optimal rates under some standard statistical assumptions. Finally, this paper suggests a way to explain the phenomenon of adversarial ex- amples, which are seemingly ubiquitous in modern machine learning, and also discusses some connections to kernel machines and random forests in the interpo- lated regime. 1 Introduction The central problem of supervised inference is to predict labels of unseen data points from a set of labeled training data. The literature on this subject is vast, ranging from classical parametric and non-parametric statistics [48, 49] to more recent machine learning methods, such as kernel machines [39], boosting [36], random forests [15], and deep neural networks [25].
    [Show full text]
  • Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
    University of Pennsylvania ScholarlyCommons Marketing Papers Wharton Faculty Research 1-2018 Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook Dokyun Lee Kartik Hosanagar University of Pennsylvania Harikesh Nair Follow this and additional works at: https://repository.upenn.edu/marketing_papers Part of the Advertising and Promotion Management Commons, Business Administration, Management, and Operations Commons, Business Analytics Commons, Business and Corporate Communications Commons, Communication Technology and New Media Commons, Marketing Commons, Mass Communication Commons, Social Media Commons, and the Technology and Innovation Commons Recommended Citation Lee, D., Hosanagar, K., & Nair, H. (2018). Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook. Management Science, http://dx.doi.org/10.1287/mnsc.2017.2902 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/marketing_papers/339 For more information, please contact [email protected]. Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook Abstract We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as Likes, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes.
    [Show full text]
  • CDC Social Media Guidelines: Facebook Requirements and Best Practices
    Social Media Guidelines and Best Practices Facebook Purpose This document is designed to provide guidance to Centers for Disease Control and Prevention employees and contractors on the process for planning and development, as well as best practices for participating and engaging, on the social networking site Facebook. Background Facebook is a social networking service launched in February 2004. As of March 2012, Facebook has more than 901 million active users, who generate an average of 3.2 billion Likes and Comments per day. For additional information on Facebook, visit http://newsroom.fb.com/. The first CDC Facebook page, managed by the Office of the Associate Director for Communication Science (OADC), Division of News and Electronic Media (DNEM), Electronic Media Branch (EMB), was launched in May 2009 to share featured health and safety updates and to build an active and participatory community around the work of the agency. The agency has expanded its Facebook presence beyond the main CDC profile, and now supports multiple Facebook profiles connecting users with information on a range of CDC health and safety topics. Communications Strategy Facebook, as with other social media tools, is intended to be part of a larger integrated health communications strategy or campaign developed under the leadership of the Associate Director of Communication Science (ADCS) in the Health Communication Science Office (HCSO) of CDC’s National Centers, Institutes, and Offices (CIOs). Clearance and Approval 1. New Accounts: As per the CDC Enterprise Social Media policy (link not available outside CDC network): • All new Facebook accounts must be cleared by the program’s HCSO office.
    [Show full text]
  • Narcissism and Social Media Use: a Meta-Analytic Review
    Psychology of Popular Media Culture © 2016 American Psychological Association 2018, Vol. 7, No. 3, 308–327 2160-4134/18/$12.00 http://dx.doi.org/10.1037/ppm0000137 Narcissism and Social Media Use: A Meta-Analytic Review Jessica L. McCain and W. Keith Campbell University of Georgia The relationship between narcissism and social media use has been a topic of study since the advent of the first social media websites. In the present manuscript, the authors review the literature published to date on the topic and outline 2 potential models to explain the pattern of findings. Data from 62 samples of published and unpublished research (N ϭ 13,430) are meta-analyzed with respect to the relationships between grandiose and vulnerable narcissism and (a) time spent on social media, (b) frequency of status updates/tweets on social media, (c) number of friends/followers on social media, and (d) frequency of posting pictures of self or selfies on social media. Findings suggest that grandiose narcissism is positively related to all 4 indices (rs ϭ .11–.20), although culture and social media platform significantly moderated the results. Vul- nerable narcissism was not significantly related to social media use (rs ϭ .05–.42), although smaller samples make these effects less certain. Limitations of the current literature and recommendations for future research are discussed. Keywords: narcissism, social media, meta-analysis, selfies, Facebook Does narcissism relate to social media use? Or introverted form) narcissism spend more time is the power to selectively present oneself to an on social media than those low in narcissism? online audience appealing to everyone, regardless (b) Do those high in grandiose and vulnerable of their level of narcissism? Social media websites narcissism use the features of social media (i.e., such as Facebook, Twitter, or Instagram can adding friends, status updates, and posting pic- sound like a narcissistic dream.
    [Show full text]
  • Probabilistic Circuits: Representations, Inference, Learning and Theory
    Inference Probabilistic Representations Learning Circuits Theory Antonio Vergari YooJung Choi University of California, Los Angeles University of California, Los Angeles Robert Peharz Guy Van den Broeck TU Eindhoven University of California, Los Angeles January 7th, 2021 - IJCAI-PRICAI 2020 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs The Alphabet Soup of probabilistic models 2/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs Intractable and tractable models 3/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs tractability is a spectrum 4/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs Expressive models without compromises 5/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs a unifying framework for tractable models 6/153 Why tractable inference? or expressiveness vs tractability 7/153 Why tractable inference? or expressiveness
    [Show full text]
  • Risk Bounds for Classification and Regression Rules That Interpolate
    Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate Mikhail Belkin1, Daniel Hsu2, and Partha P. Mitra3 1The Ohio State University, Columbus, OH 2Columbia University, New York, NY 3Cold Spring Harbor Laboratory, Cold Spring Harbor, NY October 29, 2018 Abstract Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phenomenon of strong generalization performance for “overfitted” / interpolated classifiers appears to be ubiquitous in high-dimensional data, having been observed in deep networks, kernel machines, boosting and random forests. Their performance is consistently robust even when the data contain large amounts of label noise. Very little theory is available to explain these observations. The vast majority of theoretical analyses of generalization allows for interpolation only when there is little or no label noise. This paper takes a step toward a theoretical foundation for interpolated classifiers by analyzing local interpolating schemes, including geometric simplicial interpolation algorithm and singularly weighted k-nearest neighbor schemes. Consistency or near-consistency is proved for these schemes in classification and regression problems. Moreover, the nearest neighbor schemes exhibit optimal rates under some standard statistical assumptions. Finally, this paper suggests a way to explain the phenomenon of adversarial examples, which are seemingly ubiquitous in modern machine learning, and also discusses some connections to kernel machines and random forests in the interpolated regime. 1 Introduction arXiv:1806.05161v3 [stat.ML] 26 Oct 2018 The central problem of supervised inference is to predict labels of unseen data points from a set of labeled training data.
    [Show full text]
  • Implementation on Social Media Platforms
    Implementation on Social Media Platforms Overview The Eko Video Player is whitelisted for the Twitter and Facebook environments. When end viewers share an Eko video to one of these services, they essentially embed the Eko Video Player onto their timeline. Depending on the service (i.e. Facebook or Twitter), the end viewer’s platform, and whether the post is promoted (on Facebook only), the Eko Video Player will either play inline within the social media feed, in a new tab within the social media space, or in a new browser tab. Support for the Eko Video Player within these services is made possible by meta tags that are added to the HTML of the site where the video is hosted. These meta tags enable a Facebook Preview and Twitter Card to display in the feed when the video site url is shared on these services. The cards link directly to the interactive video object, enable playback, and specify the sharing copy for the content (provided by the partner). Direct links to videos hosted by Eko automatically include the meta tags to enable inline playback on shared posts, but for videos embedded outside of helloeko.com on a client’s website, the meta tags will need to be added to the website in the <head> part of the page’s HTML and tested. Facebook Open Graph Meta Tags When someone shares a video project hosted on your site to Facebook, Facebook’s crawler will scrape the HTML of the URL that is shared. On a regular HTML page this content is basic and may be incorrect, because the scraper has to guess which content is important, and which is not.
    [Show full text]
  • Part I Officers in Institutions Placed Under the Supervision of the General Board
    2 OFFICERS NUMBER–MICHAELMAS TERM 2009 [SPECIAL NO.7 PART I Chancellor: H.R.H. The Prince PHILIP, Duke of Edinburgh, T Vice-Chancellor: 2003, Prof. ALISON FETTES RICHARD, N, 2010 Deputy Vice-Chancellors for 2009–2010: Dame SANDRA DAWSON, SID,ATHENE DONALD, R,GORDON JOHNSON, W,STUART LAING, CC,DAVID DUNCAN ROBINSON, M,JEREMY KEITH MORRIS SANDERS, SE, SARAH LAETITIA SQUIRE, HH, the Pro-Vice-Chancellors Pro-Vice-Chancellors: 2004, ANDREW DAVID CLIFF, CHR, 31 Dec. 2009 2004, IAN MALCOLM LESLIE, CHR, 31 Dec. 2009 2008, JOHN MARTIN RALLISON, T, 30 Sept. 2011 2004, KATHARINE BRIDGET PRETTY, HO, 31 Dec. 2009 2009, STEPHEN JOHN YOUNG, EM, 31 July 2012 High Steward: 2001, Dame BRIDGET OGILVIE, G Deputy High Steward: 2009, ANNE MARY LONSDALE, NH Commissary: 2002, The Rt Hon. Lord MACKAY OF CLASHFERN, T Proctors for 2009–2010: JEREMY LLOYD CADDICK, EM LINDSAY ANNE YATES, JN Deputy Proctors for MARGARET ANN GUITE, G 2009–2010: PAUL DUNCAN BEATTIE, CC Orator: 2008, RUPERT THOMPSON, SE Registrary: 2007, JONATHAN WILLIAM NICHOLLS, EM Librarian: 2009, ANNE JARVIS, W Acting Deputy Librarian: 2009, SUSANNE MEHRER Director of the Fitzwilliam Museum and Marlay Curator: 2008, TIMOTHY FAULKNER POTTS, CL Director of Development and Alumni Relations: 2002, PETER LAWSON AGAR, SE Esquire Bedells: 2003, NICOLA HARDY, JE 2009, ROGER DERRICK GREEVES, CL University Advocate: 2004, PHILIPPA JANE ROGERSON, CAI, 2010 Deputy University Advocates: 2007, ROSAMUND ELLEN THORNTON, EM, 2010 2006, CHRISTOPHER FORBES FORSYTH, R, 2010 OFFICERS IN INSTITUTIONS PLACED UNDER THE SUPERVISION OF THE GENERAL BOARD PROFESSORS Accounting 2003 GEOFFREY MEEKS, DAR Active Tectonics 2002 JAMES ANTHONY JACKSON, Q Aeronautical Engineering, Francis Mond 1996 WILLIAM NICHOLAS DAWES, CHU Aerothermal Technology 2000 HOWARD PETER HODSON, G Algebra 2003 JAN SAXL, CAI Algebraic Geometry (2000) 2000 NICHOLAS IAN SHEPHERD-BARRON, T Algebraic Geometry (2001) 2001 PELHAM MARK HEDLEY WILSON, T American History, Paul Mellon 1992 ANTHONY JOHN BADGER, CL American History and Institutions, Pitt 2009 NANCY A.
    [Show full text]
  • Machine Learning Conference Report
    Part of the conference series Breakthrough science and technologies Transforming our future Machine learning Conference report Machine learning report – Conference report 1 Introduction On 22 May 2015, the Royal Society hosted a unique, high level conference on the subject of machine learning. The conference brought together scientists, technologists and experts from across academia, industry and government to learn about the state-of-the-art in machine learning from world and UK experts. The presentations covered four main areas of machine learning: cutting-edge developments in software, the underpinning hardware, current applications and future social and economic impacts. This conference is the first in a new series organised This report is not a verbatim record, but summarises by the Royal Society, entitled Breakthrough Science the discussions that took place during the day and the and Technologies: Transforming our Future, which will key points raised. Comments and recommendations address the major scientific and technical challenges reflect the views and opinions of the speakers and of the next decade. Each conference will focus on one not necessarily that of the Royal Society. Full versions technology and cover key issues including the current of the presentations can be found on our website at: state of the UK industry sector, future direction of royalsociety.org/events/2015/05/breakthrough-science- research and the wider social and economic implications. technologies-machine-learning The conference series is being organised through the Royal Society’s Science and Industry programme, which demonstrates our commitment to reintegrate science and industry at the Society and to promote science and its value, build relationships and foster translation.
    [Show full text]
  • AAAI News AAAI News
    AAAI News AAAI News Winter News from the Association for the Advancement of Artificial Intelligence AAAI-18 Registration Student Activities looking for internships or jobs to meet with representatives from over 30 com - As part of its outreach to students, Is Open! panies and academia in an informal AAAI-18 will continue several special "meet-and-greet" atmosphere. If you AAAI-18 registration information is programs specifically for students, are representing a company, research now available at aaai.org/aaai18, and including the Doctoral Consortium, organization or university and would online registration can be completed at the Student Abstract Program, Lunch like to participate in the job fair, please regonline.com/aaai18. The deadline with a Fellow, and the Volunteer Pro - send an email with your contact infor - for late registration rates is January 5, gram, in addition to the following: 2018. Complete tutorial and workshop mation to [email protected] no later than January 5. The organizers of information, as well as other special Student Reception the AAAI/ACM SIGAI Job Fair are John program information is available at AAAI will welcome all students to Dickerson (University of Maryland, these sites. AAAI-18 by hosting an evening stu - USA) and Nicholas Mattei (IBM, USA). dent reception on Friday, February 2. Make Your Hotel Reservation Although the reception is especially The Winograd Now! beneficial to new students at the con - Schema Challenge AAAI has reserved a block of rooms at ference, all are welcome! Please join us and make all the newcomers welcome! Nuance Communications, Inc. is spon - the Hilton New Orleans Riverside at soring a competition to encourage reduced conference rates.
    [Show full text]
  • Probabilistic Machine Learning: Foundations and Frontiers
    Probabilistic Machine Learning: Foundations and Frontiers Zoubin Ghahramani1,2,3,4 1 University of Cambridge 2 Alan Turing Institute 3 Leverhulme Centre for the Future of Intelligence 4 Uber AI Labs [email protected] http://mlg.eng.cam.ac.uk/zoubin/ Sackler Forum, National Academy of Sciences Washington DC, 2017 Machine Learning Zoubin Ghahramani 2 / 53 Many Related Terms Statistical Modelling Artificial Intelligence Neural Networks Machine Learning Data Mining Data Analytics Deep Learning Pattern Recognition Data Science Zoubin Ghahramani 3 / 53 Many Related Fields Engineering Computer Science Statistics Machine Learning Computational Applied Mathematics Neuroscience Economics Cognitive Science Physics Zoubin Ghahramani 4 / 53 Many Many Applications Bioinformatics Computer Vision Robotics Scientific Data Analysis Natural Language Processing Machine Learning Information Retrieval Speech Recognition Recommender Systems Signal Processing Machine Translation Medical Informatics Targeted Advertising Finance Data Compression Zoubin Ghahramani 5 / 53 Machine Learning • Machine learning is an interdisciplinary field that develops both the mathematical foundations and practical applications of systems that learn from data. Main conferences and journals: NIPS, ICML, AISTATS, UAI, KDD, JMLR, IEEE TPAMI Zoubin Ghahramani 6 / 53 Canonical problems in machine learning Zoubin Ghahramani 7 / 53 Classification x x x o x xxx o x o o o x x o o x o • Task: predict discrete class label from input data • Applications: face recognition, image recognition,
    [Show full text]
  • Bayesian Activity Recognition Using Variational Inference
    BAR: Bayesian Activity Recognition using variational inference Ranganath Krishnan Mahesh Subedar Omesh Tickoo [email protected] [email protected] [email protected] Intel Labs Hillsboro, OR (USA) Abstract Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activities are designed with deep neural networks (DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable uncertainty estimates in safety and security critical applications will help to establish trust in the AI system. Our contribution is to apply Bayesian deep learning framework to visual activity recognition application and quantify model uncertainty along with principled confidence. We utilize the stochastic variational inference technique while training the Bayesian DNNs to infer the approximate posterior distribution around model parameters and perform Monte Carlo sampling on the posterior of model parameters to obtain the predictive distribution. We show that the Bayesian inference applied to DNNs provide reliable confidence measures for visual activity recognition task as compared to conventional DNNs. We also show that our method improves the visual activity recognition precision-recall AUC by 6.2% compared to non-Bayesian baseline. We evaluate our models on Moments-In-Time (MiT) activity recognition dataset by selecting a subset of in- and out-of-distribution video samples. 1 Introduction Activity recognition is an active area of research with multiple approaches depending on the applica- tion domain and the types of sensors [1, 2, 3]. In recent years, the DNN models applied to the visual activity recognition task outperform the earlier methods based on Gaussian Mixture Models and Hid- den Markov Models [4, 5] using handcrafted features.
    [Show full text]