Microsoft Research Blog

Artificial intelligence

  1. Usability and acceptability of ASSESS MS: a system to support the assessment of motor dysfunction in Multiple Sclerosis using depth-sensing computer vision 

    May 1, 2015

    Background: Sensor-based recordings of human movements are becoming increasingly important for the assessment of motor symptoms in neurological disorders beyond rehabilitative purposes. ASSESS MS is a movement recording and analysis system being developed to automate the classification of motor dysfunction in patients with multiple sclerosis…

  2. Improved mispronunciation detection with deep neural network trained acoustic models and transfer learning based logistic regression classifiers 

    February 28, 2015 | Wenping Hu, Yao Qian, Frank Soong, and Yong Wang

    Abstract Mispronunciation detection is an important part in a Computer-Aided Language Learning (CALL) system. By automatically pointing out where mispronunciations occur in an utterance, a language learner can receive informative and to-the-point feedbacks. In this paper, we improve mispronunciation detection performance with a Deep Neural…

  3. Entity centric Feature Pooling for Complex Event Detection 

    November 6, 2014 | Ishani Chakraborty, Hui Cheng, and Omar Javed

    In this paper, we propose an entity centric region of interest detection and visual-semantic pooling scheme for complex event detection in YouTube-like videos. Our method is based on the hypothesis that many YouTube-like videos involve people interacting with each other and objects in their vicinity.…

  4. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 

    October 1, 2014

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-…

  5. Knowledge Graph and Text Jointly Embedding 

    September 30, 2014 | Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen

    We examine the embedding approach to reason new relational facts from a largescale knowledge graph and a text corpus. We propose a novel method of jointly embedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between…

  6. Classifier-Based Multi-atlas Label Propagation with Test-Specific Atlas Weighting for Correspondence-Free Scenarios 

    September 17, 2014 | Darko Zikic, Ben Glocker, and Antonio Criminisi

    We propose a segmentation method which transfers the advantages of multi-atlas label propagation (MALP) to correspondence-free scenarios. MALP is a branch of segmentation approaches with attractive properties, which is currently applicable only in correspondence-based regimes such as brain labeling, which assume correspondence between atlases and…

  8. Structured Generative Models of Natural Source Code 

    June 20, 2014 | Chris J. Maddison and Daniel Tarlow

    We study the problem of building generative models of natural source code (NSC); that is, source code written by humans and meant to be understood by humans. Our primary contribution is to describe new generative models that are tailored to NSC. The models are based…

  9. General-purpose code acceleration with limited-precision analog computation 

    June 1, 2014

    As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution-from circuit to compiler-that enables general-purpose use of limited-precision, analog hardware to accelerate “approximable” code-code…

  11. Conformal predictors for online track classification 

    May 3, 2014 | Michael J. Pekala, I-Jeng Wang, and Ashley J. Llorens

    This paper considers online classification problems where each object to be classified consists of a sequence of measurements, termed here a track. We present an approach that combines ideas from sequential hypothesis testing with those from conformal prediction to address track level outliers - entire…

  12. RTSMate: Towards an Advice System for RTS Games 

    February 28, 2014 | Renato L. de F. Cunha, Marlos C. Machado, and Luiz Chaimowicz

    Real Time Strategy (RTS) games can be very challenging, especially to novice users, who are normally overwhelmed by the dynamic, distributed, and multi-objective structure of these games. In this paper we present RTSMate, an advice system designed to help the player of an RTS game.…