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Our research
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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.
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Open source is a powerful way to advance software development and share data for experimentation. Microsoft has research projects as well as key software—such as .NET—in open-source repositories. These tools provide opportunities for engaging with Microsoft researchers and advancing the state of the art together. The powerful software can be used in classes and projects to prepare students for real-world applications.
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Labs: India | Redmond
Machine Learning-Based Predictive Modelling of CRISPR/Cas9 guide efficiency
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This project uses autonomous methods such as drone-enabled, mosquito-collection devices/traps to collect mosquitos and then conduct cloud-based, next-generation gene sequencing analysis of pathogens in the mosquito blood to detect early signs of infectious diseases.
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Labs: Redmond
File System for Approximate Storage
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Labs: Redmond
Microsoft Research is conducting a study of how people share both physical and digital things in home settings. We’re interested in finding out what makes it easy (or difficult) for people to share ownership of physical or digital belongings in order to better understand how to support people in sharing digital things in the home.
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Labs: Cambridge
To facilitate ethics reviews for MSR research projects, we create a new ethics framework specific to Computer Science research.
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Researchers who can best capture, process, predict, and visualize data will be able to accelerate their work and push the boundaries of what is possible. We want to help you take advantage of our research and development efforts so that you can spend more time doing data science. Besides a range of tools and services, we provide valuable datasets across many areas of interest. You can download more than 40 datasets from this website and nearly 150 from the Azure Marketplace. Learn more.
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Labs: Redmond
Robust Distributed System Nucleus (rDSN) is an open framework for quickly building and managing high performance and robust distributed systems. The core idea is a coherent and principled design that distributed systems, tools, and frameworks can be developed independently and later on integrated (almost) transparently.
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Labs: Asia
This page contains references to work on "consensus problem", a distributed computation problem that has been studied in the context of distributed computing systems, population protocols, biological computations, and social networks. The problem is often referred to as consensus or majority computation or quantized averaging, and is intimately related to voter problem, quantile computation, selection problem, local interaction, and coordination games.
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Labs: Cambridge
Graph Engine, previously known as Trinity, is a distributed, in-memory, large graph processing engine.
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Labs: Asia
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.
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Labs: Redmond
Our goal is to let normal users tell computers what to do using normal language. This problem space is strongly related to natural language understanding, program synthesis, and many other areas.
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Labs: Redmond
The RoomAlive Toolkit is an open source SDK that enables developers to calibrate a network of multiple Kinect sensors and video projectors. The toolkit also provides a simple projection mapping sample that can be used as a basis to develop new immersive augmented reality experiences similar to those of the IllumiRoom and RoomAlive research projects.
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Labs: Redmond
Project details
Labs: Redmond
Logic flaws are prevalent in multiparty cloud services, which cause serious consequences, e.g., an attacker can make purchases without paying, or gets into other people’s accounts without password. For decades, researchers have been advocating formal verification as a solution, but in the real world developers face many major hurdles to do it. We introduce a technology that significantly lowers these hurdles, and show its effectiveness in real-world deployments.
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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.
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Labs: Redmond
The proliferation of connected devices can in theory enable a range of applications that make rich inferences about users and their environment. But in practice developing such applications today is arduous because they are constructed as monolithic silos, tightly coupled to sensing devices, and must implement all sensing & inference logic, even as devices move or are temporarily disconnected. Our goal is to break down restrictive device-application silos and simplify app development.
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Labs: Redmond
The Kamino project explores ways in which systems should adopt new memory technologies including SSDs (NAND-Flash), battery-backed DRAM and emerging non-volatile memory technologies (phase change memory, memristors, spin-torque transfer memory, etc.) for increased performance and efficiency. The project explores how to best leverage such new memory technologies inside systems of all sizes and shapes: from mobile to data center scale.
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Labs: Redmond
We introduce a novel approach for automatically generating image descriptions. Visual detectors, language models, and deep multimodal similarity models are learned directly from a dataset of image captions. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. Human judges consider the captions to be as good as or better than humans 34% of the time.
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Labs: Redmond
Microsoft believes the Surface Hub will be as empowering and as transformative to teams and the shared work environment as the PC was to individuals and the desk. The Surface Hub creates new modalities for creating and brainstorming with its unique large-screen productivity apps and capabilities. We believe it will be a critical component for the modern workplace, home, or other venue where people need to come together to think, ideate, and produce. This RFP is now closed.
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Labs: Redmond
Project details
Labs: Redmond
The Eye Gaze keyboard is a project to enable people who are unable to speak or use a physical keyboard to communicate using only their eyes. Our initial prototypes are based around an on screen qwerty keyboard very similar to the 'taptip' keyboard built into Windows 8 which has been extended to response to eye gaze input from a sensor bar like the Tobii EyeX. Our goal is to improve communication speed by 25% compared to experienced users of off the shelf Speech Generating Devices.
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.
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Labs: Redmond
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