13,793 research outputs found
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Progression to macula-off tractional retinal detachment after a contralateral intraoperative intravitreal bevacizumab injection for proliferative diabetic retinopathy
No full textMichael W Stewart, Michael L Stewart Department of Ophthalmology, Mayo Clinic, Jacksonville, FL, USA In a recent edition of Clinical Ophthalmology, Zlotcavitch et al presented a case of progressive diabetic traction retinal detachment in the fellow eye 1 week after vitrectomy with intravitreal bevacizumab.1 This interesting observation extends previous original work by the same authors in which proliferative diabetic retinopathy was noted to regress following a bevacizumab injection into the fellow eye.2 Several points pertaining to this thought-provoking report deserve further discussion. Read the original article 
Does Age Affect Treatment of Depressive Disorders? A systematic review of antidepressants versus placebo studies
No full textOptimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
Topical azithromycin or ofloxacin for endophthalmitis
No full textMichael W Stewart, Michael L StewartDepartment of Ophthalmology, Mayo Clinic, Jacksonville, FL, USAThe recently published study by Romero-Aroca et al1 raises interesting questions regarding the effect of choice of topical antibiotic (azithromycin versus ofloxacin) on the incidence of endophthalmitis following intravitreal injections. However, important conclusions advanced by the authors deserve further discussion. First, the authors state that use of azithromycin leads to significantly fewer cases of post-injection endophthalmitis than does the use of ofloxacin. Their prospective series shows a lower endophthalmitis rate in eyes treated with azithromycin (two cases in 4045 injections, 0.049%) than in eyes treated with ofloxacin (five cases in 4151 injections, 0.12%). They calculate a relative risk of 2.37, and conclude that this was statistically significant (confidence interval 1.37–3.72; P < 0.001).View original paper by Romero-Aroca and colleagues
ML@GT Lab presents LAB LIGHTNING TALKS 2020
No full textPresented online on December 4, 2020 at 2:00 p.m.Professor Ghassan AlRegib is currently a professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He is the director of the Multimedia and Sensors Lab (MSL) at Georgia Tech.Duen Horng (Polo) Chau is an Associate Professor of Computing at Georgia Tech. He is the Machine Learning Area Leader of the college. And He co-directs Georgia Tech's MS Analytics program. His research bridges data mining and human-computer interaction (HCI) to create scalable interactive tools for making sense of massive datasets and solving real world problems.Sudheer Chava is the Alton M. Costley Chair and a professor of finance in the Scheller College of Business at Georgia Tech. His research research interests are in credit risk, banking, empirical asset pricing and corporate finance.Morris B. Cohen is an Associate Professor in Electrical and Computer Engineering at Georgia Tech. His scientific interests include low-frequency radio wave generation, propagation and remote sensing, including applications to lightning characterization, ionospheric physics, space weather and space plasma physics. He also studies novel methods for efficient broadband electrically short antennas, and imaging through electric conductors.Mark A. Davenport is an Associate Professor with the School of Electrical and Computer Engineering, Georgia Institute of Technology. His primary area of research concerns the fundamental role that low-dimensional models and optimization play in signal processing, statistical inference, and machine learning.
Prof. Davenport is a recipient of the National Science Foundation CAREER award, the Air Force Office of Scientific Research Young Investigator award, and a Sloan Research Fellowship.Deven Desai is faculty in the Scheller College of Business Law and Ethics Program at the Georgia Institute of Technology. He was also the first, and to date, only academic research counsel at Google, Inc., and a visiting Fellow at Princeton University's Center for Information Technology Policy.
Desai's scholarship examines how business interests, new technology, and economic theories shape privacy and intellectual property law and where those arguments explain productivity or where they fail to capture society's interest in the free flow of information and development.Constantine Dovrolis is a Professor at the School of Computer Science of the Georgia Institute of Technology. His current research focuses on cross-disciplinary applications of network analysis and data mining in neuroscience and biology. He has also worked on the evolution of the Internet, Internet economics, and on applications of network measurement.Irfan Essa is a Distinguished Professor in the School of Interactive Computing (iC) and a Senior Associate Dean in the College of Computing (CoC), at the Georgia Institute of Technology (GA Tech), in Atlanta, Georgia, USA. He is serving as the Inaugural Executive Director of the new Interdisciplinary Research Center for Machine Learning at Georgia Tech (ML@GT).Dr. Swati Gupta is a Fouts Family Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Gupta's research interests lie primarily in combinatorial, convex, and robust optimization with applications in online learning and data-driven decision-making under partial information. Her work focuses on speeding up fundamental bottlenecks that arise in learning problems due to the combinatorial nature of the decisions, as well as drawing from machine learning to improve traditional optimization methods.Xiaoming Huo is an A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.
Dr. Huo's research interests include statistical theory, statistical computing, and issues related to data analytics. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited.Dr. Zsolt Kira is an Assistant Professor at the Georgia Institute of Technology, branch chief of the Machine Learning and Analytics group at the Georgia Tech Research Institute (GTRI), and Associate Director of Georgia Tech’s Machine Learning Center. His work lies at the intersection of machine learning and artificial intelligence for sensor processing, perception, and robotics, emphasizing the fusion of multiple sources of information for scene understanding.Jing Li is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Li’s research develops statistical machine learning algorithms for modeling and inference of medical image data, and fusion of images, genomics, and clinical records for personalized and precision medicine. Her research outcomes support clinical decision making for diagnosis, prognosis, and telemedicine for various conditions affecting the brain, such as brain cancer, post-traumatic headache & migraine, traumatic brain injury, and the Alzheimer’s disease.Dr. Siva Theja Maguluri is a Fouts Family Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Maguluri's research interests are broadly in Applied Probability and Optimization, and include fundamental problems in Queueing Theory, Stochastic Optimization, Distributed Optimization, Reinforcement Learning and Game Theory. He also uses these tools to work on applied problems including Scheduling, Resource Allocation and Revenue Optimization in a variety of systems including Data Centers, Cloud Computing, Wireless Networks, Block Chains, Ride hailing systems etc.Ashwin Pananjady is an Assistant Professor at Georgia Tech with a joint appointment between the H. Milton Stewart School of Industrial and Systems Engineering and the School of Electrical and Computer Engineering. His research interests lie broadly in statistics, optimization, and information theory, as well as their applications in data science, machine learning, and reinforcement learning. He is particularly interested in statistical and computational problems arising from high-dimensional data with geometric structure.B. Aditya Prakash is an Associate Professor in the College of Computing at the Georgia Institute of Technology (“Georgia Tech”). His research interests include Data Science, Machine Learning and AI, with emphasis on big-data problems in large real-world networks and time-series, with applications to epidemiology, health, urban computing, security and the Web.Mark Riedl is an associate professor in the College of Computing, School of Interactive Computing. As director of the Entertainment Intelligence Lab, Dr. Riedl's research focuses on the study of artificial intelligence and storytelling for entertainment (e.g., computer games). Narrative is a cognitive tool used by humans for communication, sense-making, entertainment, education, and training. Consequently, there is value in discovering new computational techniques that make computers better communicators, entertainers, and educators. The principle research question Dr. Riedl addresses through his research is: how can intelligent computational systems reason about and autonomously create engaging experiences for users of virtual worlds and computer games?Dr. Justin Romberg (Moderator) is the Schlumberger Professor and the Associate Chair for Research in the School of Electrical and Computer Engineering and the Associate Director for the Center for Machine Learning at Georgia Tech. His research interests lie on the intersection of signal processing, machine learning, optimization, and applied probability.Yao Xie is the Harold R. and Mary Anne Nash Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.
Her research interests are in sequential statistical methods, statistical signal processing, big data analysis, compressed sensing, optimization, and has been involved in applications to wireless communications, sensor networks, medical and astronomical imaging.Xiuwei Zhang is an Assistant Professor at the School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. My research group works on applying machine learning and optimization skills in method development and data analysis for single-cell RNA-Seq data and other types of data on single cell level. The goal is to study cellular mechanisms during differentiation, development of cells and disease progression.Runtime: 74:19 minutesLabs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to interested graduate students, Georgia Tech faculty, and members of the public. Participating labs include:
Yao’s Group - Yao Xie, H. Milton Stewart School of Industrial Systems and Engineering (ISyE);
Huo Lab - Xiaoming Huo, ISyE;
LF Radio Lab – Morris Cohen, School of Electrical Computing and Engineering (ECE);
Polo Club of Data Science – Polo Chau, CSE;
Network Science – Constantine Dovrolis, School of Computer Science;
CLAWS – Srijan Kumar, CSE;
Control, Optimization, Algorithms, and Randomness (COAR) Lab – Siva Theja Maguluri, ISyE;
Entertainment Intelligence Lab and Human Centered AI Lab – Mark Riedl, IC;
Social and Language Technologies (SALT) Lab – Diyi Yang, IC;
FATHOM Research Group – Swati Gupta, ISyE;
Zhang's CompBio Lab – Xiuwei Zhang, CSE;
Statistical Machine Learning - Ashwin Pananjady, ISyE and ECE;
AdityaLab - B. Aditya Prakash, CSE;
OLIVES - Ghassan AlRegib, ECE;
Robotics Perception and Learning (RIPL) – Zsolt Kira, IC;
Eye-Team - Irfan Essa, IC; and
Mark Davenport, ECE
Building a generalisable ML pipeline at ING
No full textAdvances in data science have caused an increase in the use of Artificial Intelligence (AI), specifically Machine Learning (ML), throughout various fields. Not only in research but in the industry as well, has ML been receiving increasing amounts of interest. Many companies rely on ML models to increase the efficiency of existing processes or offer new services and products. The industry, however, is facing several additional challenges compared to the academic context. One of those challenges is applying the Development Operations (DevOps) model to an ML application, also referred to as MLOps. This thesis sets out to find the specific challenges that practitioners encounter while operationalising ML models. To do so, we perform a single-case case study on an ML pipeline built by the Trade & Communication Surveillance team at the ING bank. This case study consists of conducting a set of interviews and performing a manual code inspection of the pipeline. The team faces challenges ranging from having insufficient time for operationalising each ML project individually to operating in the highlyregulated fintech context. Their pipeline is able to deploy a single ML model but it does not generalise well to other projects. We present the first version of an application that mitigates these challenges. The application is able to deploy ML models to the development environment at ING and can be operated by data scientists to reduce the effort of operationalising an ML model. Computer Science | Software Technolog
'Project smells' - Experiences in Analysing the Software Quality of ML Projects with mllint
No full textMachine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software EngineeringSoftware Technolog
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