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ViiBoard uses vision techniques to significantly enhance the user experience on large touch displays (e.g. Microsoft Perceptive Pixels) in two directions: human computer interaction and immersive remote collaboration. the first
Alternating minimization is a popular approach to solve several optimization problems. In this work, we explore theoretical properties of this method (and its variants) for several non-convex optimization problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc.
Microsoft Research in partnership with Bing is happy to launch the second MSR-Bing Challenge on Image Retrieval. Do you have what it takes to build the best image retrieval system? Enter the MSR-Bing Image Retrieval Challenge in ACM Multimedia and/or ICME to develop an image scoring system for a search query.
Using a diversity of big data to infer and predict fine-grained air quality throughout a city, and finally tackle air pollutions.
Sequence Error (SE) Minimization Training of Neural Network for Voice Conversion
Project CodaLab is an open source platform that empowers communities to explore experiments together and create competitions designed to advance the state-of-the-art in machine learning.
Conversational systems interact with people through language to assist, enable, or entertain. Research at Microsoft spans dialogs that use language exclusively, or in conjunctions with additional modalities like gesture; where language is spoken or in text; and in a variety of settings, such as conversational systems in apps or devices, and situated interactions in the real world.
We are studying how we can get regular people to do simple tasks at specific locations. An example task is to take a picture of a sign at a certain location. We are interested in who to ask and how much to pay.
We work on questions motivated by machine learning, in particular from the theoretical and computational perspectives. Our goals are to mathematically understand the effectiveness of existing learning algorithms and to design new learning algorithms. We combine expertise from diverse fields such as algorithms and complexity, statistics, and convex geometry.
Labs: Silicon Valley
Tabular is a Excel add-in that brings the power of model based machine learning to data enthusiasts. It allows the user to write a simple model that explains their data and perform Bayesian inference. Tabular is built on top of Infer.NET.
Natural Language Processing (NLP) is a foundational infrastructure for processing written text. This processing revolves around text analysis and understanding serving a multitude of sophisticated tasks such as Text Search, Document Management, Automatic Translation, Proofreading, Text Summarization and many more…
Labs: ATL Cairo
Crowded: Digital Piecework and the Politics of Platform Responsibility in Precarious Times looks as crowdsourcing as a focal point for many of the issues that are raised by the structure of our current information economy: economic value, cultural meaning, and ethics.
Labs: New England
This is an umbrella project for our activity in machine learning with exploration-exploitation tradeoff. Most of us are at MSR-NYC.
We work on fundamental issues in crowdsourcing, in particular, incentive mechanisms for paid crowdsourcing, algorithms and theory for crowdsourced problem solving.
The LKW project is aimed at designing low-power algorithms and systems for admission control to speech systems: i.e., detecting foreground speech, recognizing leading keywords and verifying speakers on a continuously-on wearable device. Our goal is to consume under 10 mW average on generic embedded hardware available today and under 100uW on custom hardware.
Big Sky is a web service for exploratory data analysis.
The goal of this project is to provide easily usable models for lexical semantic relations, which have been developed at Microsoft Research. Currently the models include heterogeneous vector space models for measuring semantic word relatedness and the polarity inducing latent semantic analysis (LSA) model that judges whether two words or synonyms or antonyms.
GeoS is a Windows application for interactive semi automated segmentation of medical images such as CT (Computed Tomography) and MR (Magnetic Resonance) scans.
Search TrailBlazer is a project that aims at redefining the way people think about search. We propose to model user search behaivor using tasks rather than queries or sessions in the traditional way. Our framework contains components to impact multiple core areas of search engines, including relevance ranking, metric design, user satisfaction prediction, DSAT mining, competitive analysis and etc.
R2 is a research project within the Programming Languages and Tools group at Microsoft Research India on probabilistic programming. Our goal is to build a user friendly and scalable probabilistic programming system by employing powerful techniques from language design, program analysis and verification.
The 7-Scenes dataset is a collection of tracked RGB-D camera frames. The dataset may be used for evaluation of methods for different applications such as dense tracking and mapping and relocalization techniques.
There is some evidence that a gap exists between the neural network research and software development communities. Source code examples available to software developers are often incomplete, misleading, or just plain incorrect. The goal of this project is to bridge that gap by providing a series of high quality demo programs. The basic C# demo can be accessed from: http://research.microsoft.com/NeuralNetworks/BackPropDemo.aspx
Accurate localization and identification of vertebrae in spinal CT imaging is important for many clinical tasks such as diagnosis, surgical planning, and post-operative assessment. Clinical datasets raise many difficulties for automatic methods. These arise from the frequent presence of abnormal spine curvature, small field of view, and image artifacts caused by surgical implants. To facilitate the advance of research on this topic, we provide a database of 242 annotated spine CT scans.
Spoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods.