Forecasting is an important task across several domains. Its generalised interest is related to the uncertainty and evolving structure of time series. Forecasting methods are typically designed to cope with temporal dependencies among observations. In this paper we propose a novel approach where several forecasting models are dynamically combined to make a predictions.
In this talk we will present the various optimization problems encountered in smart grids from the production, transmission and distribution of energy as well as the demand side management in smart homes and the pricing of energy. The optimization opportunities are highlighted for metaheuristics, multi-objective optimization, optimization under uncertainty, optimization-simulation, optimization-machine learning and multi-level optimization.
Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. This talk will present work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions. We will look at clinical dialogue between patient and doctor or therapist, in cases involving schizophrenia, depression and dementia; in each case, we find that diagnostic information and/or treatment outcomes are related to observable features of a patient's language and interaction with their conversational partner. We discuss the nature of these phenomena and the suitability and accuracy of NLP techniques for detecting them automatically.
Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. In his talk Dr Matthew Purver from Queen Mary University of London presents work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions.
RavenPack's prestigious annual event has experienced growing interest, with attendance exceeding 260 buy-side professionals. Word on the street is RavenPack's research symposium is a "must attend event" for quantitative investors and financial professionals that are serious about Big Data. An excellent set of senior finance professionals shared their latest research and experience with big data and machine learning.
The growth of the Web is a success story that has spurred much research in knowledge discovery and data mining. Data mining over Web domains that are unusual is an even harder problem. There are several factors that make a domain unusual. In particular, such domains have significant long tails and exhibit concept drift, and are characterized by high levels of heterogeneity. Notable examples of unusual Web domains include both illicit domains, such as human trafficking advertising, illegal weapons sales, counterfeit goods transactions, patent trolling and cyberattacks, and also non-illicit domains such as humanitarian and disaster relief. Data mining in such domains has the potential for widespread social impact, and is also very challenging technically. In this tutorial, we provide an overview, using demos, examples and case studies, of the research landscape for data mining in unusual domains, including recent work that has achieved state-of-the-art results in constructing knowledge graphs in a variety of unusual domains, followed by inference and search using both command line and graphical interfaces.
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users' and items' attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user's attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition.
Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business.
Human activity recognition (HAR) plays an important role in people's daily life by learning and identifying high-level knowledge about human activity from raw sensor inputs. Conventional pattern recognition approaches have made tremendous progress on HAR tasks by adopting machine learning algorithms such as decision tree, random forest or support vector machine, but the fast development and advancement of deep learning have overpass the accuracy of traditional machine learning results. This seminar is focused on Deep learning applied to HAR using wearable sensors. Current architectures used and how to implement them for achieving good results will be explained. Limitations and new challenges will be also discussed.