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  • International Conference on Learning Representations (ICLR) 2013

    It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.

    Despite the importance of representation learning to machine learning and to application areas such as vision, speech, audio and NLP, there is currently no common venue for researchers who share a common interest in this topic. The goal of ICLR is to help fill this void.

    ICLR 2013 will be a 3-day event from May 2nd to May 4th 2013, co-located with AISTATS2013 in Scottsdale, Arizona. The conference will adopt a novel publication process, which is explained in further detail here: Publication Model.

  • Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2013

    The Sixteenth international conference on Artificial Intelligence and Statistics (AISTATS 2013) will be held in Scottsdale, AZ, USA. AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective.

  • CVPR 2013 Webcast

    CVPR is the premier annual Computer Vision event comprising the main CVPR conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. All of the tutorials and oral presentations will be live streamed.

  • Columbia-Princeton Probability Day 2013

    The workshop was held in Jadwin Hall, Room A10 on the Princeton University campus.

  • Vator Splash SFO Feb 2013

    Vator (short for innovator) is one of the largest business networks dedicated to high-tech entrepreneurs. Founded and run by veteran and award-winning journalist Bambi Francisco, Vator is an entrepreneur and investor community, with some 100,000 members and high-tech companies. VatorNews is Vator's news site focused on innovation with about 500 contributors.

  • WritersUA 2013

    The Conference for Software User Assistance

  • 2nd Lisbon Machine Learning School (2012)

    LxMLS 2012 took place during July 19-25 at Instituto Superior Técnico, a leading Engineering and Science school in Portugal. It is organized jointly by IST, the Instituto de Telecomunicações and the Spoken Language Systems Lab - L2F of INESC-ID. Click here for information about past editions (LxMLS 2011) and to watch the videos of the lectures.


    In our second year, the topic of the school is Taming the Social Web.


    The school covers a range of machine learning (ML) Topics, from theory to practice, that are important in solving natural language processing (NLP) problems that arise in the analysis and use of Web data.

  • Machine Learning in Computational Biology (MLCB) 2012

    The field of computational biology has seen dramatic growth over the past few years, both in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data are often relationally structured and highly diverse, well-suited to approaches that combine multiple weak evidence from heterogeneous sources. These data may include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein expression data, protein sequence and 3D structural data, protein interactions, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of textual data in the biological and medical literature. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources. Furthermore, next generation sequencing technologies are yielding terabyte scale data sets that require novel algorithmic solutions.

    The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. We will invite several speakers from the biology/bioinformatics community who will present current research problems in bioinformatics, and we will invite contributed talks on novel learning approaches in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from standard approaches. Kernel methods, graphical models, feature selection, and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences.