Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Our research
Content type
+
Downloads (454)
+
Events (444)
 
Groups (151)
+
News (2728)
 
People (741)
 
Projects (1103)
+
Publications (12546)
+
Videos (5665)
Labs
Research areas
Algorithms and theory47205 (82)
Communication and collaboration47188 (102)
Computational linguistics47189 (49)
Computational sciences47190 (80)
Computer systems and networking47191 (279)
Computer vision208594 (76)
Data mining and data management208595 (16)
Economics and computation47192 (19)
Education47193 (34)
Gaming47194 (45)
Graphics and multimedia47195 (132)
Hardware and devices47196 (98)
Health and well-being47197 (30)
Human-computer interaction47198 (296)
Machine learning and intelligence47200 (173)
Mobile computing208596 (13)
Quantum computing208597 (0)
Search, information retrieval, and knowledge management47199 (199)
Security and privacy47202 (88)
Social media208598 (9)
Social sciences47203 (101)
Software development, programming principles, tools, and languages47204 (196)
Speech recognition, synthesis, and dialog systems208599 (11)
Technology for emerging markets208600 (4)
1–25 of 173
Sort
Show 25 | 50 | 100
1234567Next 
We present a new interactive approach to 3D scene understanding. Our system, SemanticPaint, allows users to simultaneously scan their environment, whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the environment. Unlike offline systems, where capture, labeling and batch learning often takes hours or even days to perform, our approach is fully online.
Project details
To facilitate ethics reviews for MSR research projects, we create a new ethics framework specific to Computer Science research.
Project details
The Microsoft Academic Graph is a heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals and conference "venues" and fields of study. This data is available as a set of zipped text files stored in Microsoft Azure blob storage and available via HTTP. The file size is ~37GB.
Project details
Labs: Redmond
Project details
Labs: Redmond
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The system leverages big data to find examples that maximize the training value of its interaction with the teacher.
Project details
Labs: Redmond
This project aims to enable people to converse with their devices. We are trying to teach devices to engage with humans using human language in ways that appear seamless and natural to humans. Our research focuses on statistical methods by which devices can learn from human-human conversational interactions and can situate responses in the verbal context and in physical or virtual environments.
Project details
Labs: Redmond
This is a project looking into design and evaluation of efficient and deployable algorithms for assignment of complex workloads to resources in modern cloud service platforms.
Project details
Labs: Cambridge
Deep Structured Semantic Model / Deep Semantic Similarity Model
Project details
Labs: Redmond
FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) is a set of tools for performing genome-wide association studies (GWAS) on large data sets. FaST-LMM runs on both Windows and Linux, and contains code to do (1) univariate GWAS, (2) testing sets of SNPs, (3) feature selection for background correction, (4) epistatic association scans, (5) a correction method for cellular heterogeneity in methylation and similar data.
Project details
Team Three Rs is a group of Microsoft Researchers working on the Global Learning XPRIZE challenge, which aims to create software to help children in the developing world achieve success in learning the "Three Rs" (Reading, Writing, and Arithmetic.
Project details
We present a new real-time articulated hand tracker which can enable new possibilities for human-computer interaction (HCI). Our system accurately reconstructs complex hand poses across a variety of subjects using only a single depth camera. It also allows for a high-degree of robustness, continually recovering from tracking failures. However, the most unique aspect of our tracker is its flexibility in terms of camera placement and operating range.
Project details
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The system leverages big data to find examples that maximize the training value of its interaction with the teacher.
Project details
Labs: New York | Redmond
CityNoise is a project led by Dr. Yu Zheng in Microsoft Research. The project aims to diagnose a city's noise pollution with crowdsensing and ubiquitous data. It reveals the fine-grained noise situation throughout a city and analyzes the composition of noises in a particular location, by using 311 complaint data together with road network data, points of interests, and social media.
Project details
Labs: Asia | New York
We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute, metric depth in real-time. We demonstrate a variety of human computer interaction scenarios.
Project details
Website for the CIKM2014 tutorial on Deep Learning for Natural Language Processing: Theory and Practice (more content to be added)
Project details
Labs: Redmond
This paper presents a method for acquiring dense nonrigid shape and deformation from a single monocular depth sensor. We focus on modeling the human hand, and assume that a single rough template model is available. We combine and extend existing work on model-based tracking, subdivision surface fitting, and mesh deformation to acquire detailed hand models from as few as 15 frames of depth data.
Project details
Labs: Cambridge
We introduce an efficient camera relocalization approach which can be easily integrated into real-time 3D reconstruction methods, such as KinectFusion. Our approach makes use of compact encoding of whole image frames which enables both online harvesting of keyframes in tracking mode, and fast retrieval of pose proposals when tracking is lost. The encoding scheme is based on randomized ferns and simple binary feature tests.
Project details
Labs: Cambridge
We publish a subset of the data from the paper "Discriminative Ferns Ensemble for Hand Pose Recognition".
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
Project details
Labs: Redmond
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.
Project details
Labs: India
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. Last Challenge: MSR-Bing IRC @ ACM Multimedia 2014. Current Challenge: MSR-Bing IRC @ ICME 2015. Next Challenge: MSR-Bing IRC @ ACM Multimedia 2015
Project details
Labs: Redmond
Project details
Labs: Asia
Sequence Error (SE) Minimization Training of Neural Network for Voice Conversion
Project details
Labs: Asia
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.
Project details
Labs: Redmond
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.
Project details
Labs: Redmond
1–25 of 173
Sort
Show 25 | 50 | 100
1234567Next 
> Our research