This project aims at applying recent deep learning methods for conversational understanding tasks such as Cortana.
To be completed.
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.
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.
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.
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.
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.
To be completed.
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
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.
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
Connecting the Next Billion Users to the Broadband Internet
Spatial Audio is a project about creating a 3D audio experience using headphones. Spatial audio is also known as 3D stereo sound, or simply 3D audio. The applications are augmented and virtual reality, but this technology also affects a trivial activities such as listening to music or watching a movie on the screen of your tablet.
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.
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.
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.
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?
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.
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.
Understanding Techniques and Tools for More Effective Telemetry and Log Data Analysis
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.
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.
NUIgraph is a prototype Windows 10 app for visually exploring data in order to discover and share insight.