Our main research topics are supervised learning (classifier adaptation, calibration, evaluation, ensembles, uncertainty estimation) and applications of machine learning and deep learning for machine perception, autonomous driving, neuroscience, biology and health. Current research addresses adversarial learning problems, transfer learning, data science, machine learning for … We are a group of researchers with shared interests in machine learning and cybernetics. Machine learning is the study of adaptive computational systems that improve their performance with experience.. We will also prioritize your learning and help point you in the right direction; but you need to … The mission of the Machine Learning Research Group (MLRG) is to scale Machine Learning (ML) across Oracle. Read More MLRG members' research interests cover a wide range of ML work. Journal of Machine Learning Research. ... talking to experts, or re-implementing research papers. Machine learning touches every aspect of Spotify’s business. Machine learning is the study of computational processes that find patterns and structure in data. All published papers are freely available online. DHI research and innovation Machine learning DHI is exploring the use of machine learning models to enhance capabilities for data and predictive analytics. Its goal is to educate librarians on uses of the complex techniques of machine learning and to provide a space for critically thinking both about new … Machine Learning. The Machine and Deep Learning Research Interest Group is a forum for researching potential applications of Machine and Deep Learning in library science, including discussions, publications and outreach to the wider Library community. Two Postdoctoral Research Positions Available in the Machine Learning Group August 10, 2017, 3:45 pm We are seeking two highly creative and motivated Research Assistants/Associates to join the Machine Learning Group at the University of Cambridge. We are a highly active group of researchers working on all aspects of machine learning. The Machine Learning Research Group at the Department of Computer Science, IT University of Copenhagen (ITU) does research in a wide area of topics including: statistical machine learning, deep learning, natural language processing, algorithmic game theory, algorithms for big data, automated planning, robotics and image analysis. Welcome to the Machine Learning Group (MLG). Leader of the group Machine Learning: Peter Grünwald. We work on a variety of topics including machine learning, deep learning, computer security, computer vision, and biometric. In the information age, IDA goes beyond the pure collection and organisation of data. Apple machine learning teams are engaged in state of the art research in machine learning and artificial intelligence. The group is a fusion of two former research groups from Aalto University, the Statistical Machine Learning and Bioinformatics group and the Bayesian Methodology group. News […] Learn about the latest advancements. One focus of the Wolverton group is to use machine learning to learn more about materials and to create models that can be used to discover new materials. Machine Learning Research (MLR) is a scholarly open access, peer-reviewed, and fully refereed journal. The Machine learning research and innovation group at American Family Insurance aims at providing innovative and efficient AI-and -data-science-inspired solutions to the enterprise. We have broad research interests across machine learning and its applications. Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. Delphi Research Group (Epidemiological Forecasting) Epidemiological forecasting is critically needed for decision making by public health officials, commercial and non-commercial institutions, and the general public. Research: Machine Learning Applying machine learning to chemistry problems has a rich history in the context of property prediction (i.e., the development of QSAR/QSPR models), but has only recently been extended to other aspects of organic synthesis. This journal provides a unified forum for researchers and scientists to share the latest research and developments in all areas of machine learning. The Machine Learning Research Group at UT Austin is led by Professor Raymond Mooney, and our research has explored a wide variety of issues in machine learning for over three decades.Our current research focus is natural language learning. We developed multiple award winning forecasting technologies, based on statistical machine learning and other techniques. Our research is inter-disciplinary, as we leverage methods from statistics, optimization, and computer science, towards a better understanding of the design principles behind learning algorithms. We have applied our techniques in contexts such as cybersecurity, personal integrity, intelligent cities, video surveillance, data science, citizen science, etc. These algorithms integrate insights from various fields, including … Each member may have multiple local affiliations to sub-groups in the MLRG. Most applications can be summarized under the umbrella of computational sustainability, a strongly interdisciplinary research area that uses machine learning approaches to address sustainability challenges in Aotearoa and worldwide. Our interests span theoretical foundations, optimization algorithms, and a variety of applications (vision, speech, healthcare, materials science, NLP, … Recognizing interrelationships and dependencies in the data is an important aspect, in particular, if no … Our group is interested in a broad range of theoretical aspects of machine learning as well as applications. Our projects in machine learning are often motivated by applications in communication systems and networks, online services, and social networks. We do this by undertaking fundamental ML research. In 2018, Science news named him one of the Top-10 scientists under 40 to watch. The group is concerned with questions in the area of intelligent data analysis (IDA). By leveraging on the strengths of both machine learning models and physics-based models, it is possible to transform the way data and models are used to improve predictions of water systems. We coordinate ML and AI research with labs and universities and explore how recent advances in the area could impact problems relevant to the insurance domain. The UT Machine Learning Research Group focuses on applying both empirical and knowledge-based learning techniques to natural language processing, text mining, bioinformatics, recommender systems, inductive logic programming, knowledge and theory refinement, planning, and intelligent tutoring. His broad research interests include randomized algorithms for large-scale machine learning. Group’s contact person: Pascal Poupart Group members Shai Ben-David Dan Brown Bill Cowan Ali Ghodsi Jesse Hoey Gautam Kamath Kate Larson Pascal Poupart Overview Machine learning is an area of specialization of statistics crossed with computer science, most notably with such areas as computational statistics, scientific computation, data visualization and computational Stanford Machine Learning Group Our mission is to significantly improve people's lives through our work in Artificial Intelligence. The Machine Learning Research Group comprises several groupings of Faculty, Postdocs and Students. Welcome to the homepage of the Machine Learning research group at the Institute of Computer Science, University of Tartu. The group comes together from many different departments to celebrate and promote the history of Machine Learning at the university. MDLM – The Machine Learning and Data Management Group @ SBA Research Research Topics: Machine Learning (ML) offers exciting possibilities for innovative products and improvements of existing services. Machine Learning at Cornell is a interdisciplinary learning and research group made up of over 30 Cornell University faculty and hundreds of involved students and alumni. JMLR has a commitment to rigorous yet rapid reviewing. The Machine Learning Group (MLG), founded in 2004 by G. Bontempi, is a research unit of the Computer Science Department of the ULB (Université Libre de Bruxelles, Brussels, Belgium), Faculty of Sciences, currently co-headed by Prof. Gianluca Bontempi and Prof. Tom Lenaerts. The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. His research interests are in machine learning, networking and communications, transportation, and smart grids. Our group works broadly on designing machine learning models for complex, relational, unstructured, and heterogeneous data. Much of the current excitement around machine learning is due to its impact in a broad range of applications. Welcome to the machine learning research group at Binghamton! Machine Learning Research Group The group is interested in the development and application of innovative computational learning models for the solution of computational complex problems. UiT Machine Learning Group Pushing the frontier Powered by the cool Arctic air, and located at 70° north, the core strength of the Machine Learning Group at UiT The Arctic University of Norway is in basic research for advancing statistical machine learning & AI methodology to face the societal and industrial data-driven challenges of the future. Machine learning algorithms are designed to automatically extract new knowledge out of data. Machine Learning Research Group Electronics and Communication Sciences Unit Indian Statistical Institute. 21 сентября 2020 года — Конференция Recent Advances in Machine Learning, Data Science, Intelligent Systems & Networking (MaDaIn 2020), проводимая в городе Дананг, Вьетнам 5—6 декабря, принимает статьи до 30 сентября. 30.5.2015 Xuran Zhao has been appointed to an assistant professorship at Zhejiang University of Technology. Research Our work is focused on machine learning: the problem of automatically building models which explain observed systems and predict their future behavior. Our group focuses both on designing novel algorithms for such complex interconnected data and applications of these algorithms on real-world data.
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