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This project aims at applying recent deep learning methods for conversational understanding tasks such as Cortana.
Project details
Labs: Redmond
Project details
Labs: Asia
We propose a novel learning scheme called network morphism. It morphs a parent network into a child network, allowing fast knowledge transferring. The child network is able to achieve the performance of the parent network immediately, and its performance shall continue to improve as the training process goes on. The proposed scheme allows any network morphism in an expanding mode for arbitrary non-linear neurons, including depth, width, kernel size and subnet morphing operations.
Project details
Labs: Asia
We study the problem of image captioning, i.e., automatically describing an image by a sentence. This is a challenging problem, since different from other computer vision tasks such as image classification and object detection, image captioning requires not only understanding the image, but also the knowledge of natural language. We formulate this problem as a multimodal translation task, and develop novel algorithms to solve this problem.
Project details
Labs: Asia
We study the problem of food image recognition via deep learning techniques. Our goal is to develop a robust service to recognize thousands of popular Asia and Western food. Several prototypes have been developed to support diverse applications. We are also developing a prototype called Im2Calories, to automatically calculate the calories and conduct nutrition analysis for a dish image.
Project details
Labs: Asia
The Dual Embedding Space Model (DESM) is an information retrieval model that uses two word embeddings, one for query words and one for document words. It takes into account the vector similarity between each query word vector and all document word vectors.
Project details
Speech Recognition
Project details
Labs: ATL Cairo
Pluripotency is the unique characteristic of embryonic stem (ES) cells, which demonstrate the capacity to generate all somatic cell lineages. But how ES cells decide to transition to a given adult cell type remains unknown. In this project, we combine formal verification, model-checking and model synthesis into a new tool for uncovering the transcriptional program of pluripotency: a reasoning engine for interaction networks.
Project details
Labs: Cambridge
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Labs: Asia
This project is focused on creating a low-overhead Time-Traveling Debugger in the Chakra JavaScript engine. This debugger supports reverse variations of the step forward operations in a debugger to enable a developer to easily reverse program execution time to see the exact sequence of statements and program values leading to an error. This project is being developed as part of the ChakraCore JavaScript engine (https://github.com/Microsoft/ChakraCore/tree/TimeTravelDebugging).
Project details
Labs: Redmond
Uncertainty is a C# library that uses LINQ to let developers easily express probabilistic computations and then inference over those computations. See our recorded Research In Focus talk from the Microsoft Faculty Summit (http://research.microsoft.com/apps/video/?id=251861) this past year for more information. Uncertain is on GitHub: (https://github.com/klipto/Uncertainty)
Project details
Labs: Redmond
Spark-CLR is an cross-company open source project to provide C# language bindings for Apache Spark, which is a cluster computing framework built around the core programming abstractions of Resilient Distributed Datasets (RDDs), a logical collection of data partitioned across machines, and Discretized Streams (DStreams), a temporal sequence of RDDs.
Project details
Labs: Redmond
Resource poverty in mobile devices is a fundamental constraint and not simply a temporary limitation of current technology. In this talk, I will put forth a vision and propose a technology that breaks free of this constraint. In this vision, mobile users seamlessly use nearby micro datacenters to obtain the resource benefits of cloud computing without incurring wide area network delays and jitter. Crisp interactive response for immersive applications that augment human cognition become easier to
Project details
Labs: Redmond
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Labs: India
Project details
Labs: Redmond
Seabed is a project to provide analytics over encrypted Big Data. The challenge is to develop fast yet secure cryptographic techniques that support a suite of applications such as Business Intelligence tools and large-scale Machine Learning frameworks. Currently, we are building Seabed into Apache Spark.
Project details
Labs: India
This research project investigates the design of an open source peer economy platform designed with and for service providers. This project is an early prototype of a worker dispatch system.
Project details
Labs: FUSE Labs
The Distributed Social Analytics Platform (DSoAP) project is focused on the “Huge Data” problem in social policy research caused by the breadth of data involved. Using aggregate social media data to investigate and validate social issues such as employment, health and fiscal policy requires analyzing many months or years of data. DSoAP is applying intelligent compaction, pre-indexing and distribution of data across a server cluster to achieve responsive query times for online data exploration.
Project details
Labs: Redmond
Language is one of the fundamental ways in which intelligence can be demonstrated, and seeking to build AI systems that can use language effectively helps focus our efforts on a number of hard research problems: Where does knowledge come from and how is it stored? What representations, learning, and inference are required to build flexible goal-directed conversational systems? How do we build conversational systems that people want to interact with? How do we learn from these interactions?
Project details
Labs: Cambridge
The amount of digital data produced has long been outpacing the amount of storage available. This project enables molecular-level data storage into DNA molecules by leveraging biotechnology advances in synthesizing, manipulating and sequencing DNA to develop archival storage.
Project details
Labs: Redmond
The goal of this project is to study and devise methods for the problems of low-rank matrix completion and in general, estimating low-rank matrices by using a small number of observations.
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Labs: India
Project details
Labs: Redmond
Microsoft Research is conducting a study of a new device, called a Timecard. Timecard allows you to organise photos and other content around a timeline, and display this on a dedicated screen in your home.
Project details
Labs: Cambridge
We propose a method that extends a given depth image into regions in 3D that are not visible from the point of view of the camera. The algorithm detects repeated 3D structures in the visible scene and suggests a set of 3D extension hypotheses, which are then combined together through a global 3D MRF discrete optimization. A collaboration with Simon Korman and Prof. Shai Avidan of Tel Aviv University.
Project details
Labs: Redmond
NUIgraph is a prototype Windows 10 app for visually exploring data in order to discover and share insight.
Project details
Labs: Redmond
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