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Microsoft Research Tiled-Vectors Demo
The tiled-vectors technique enables the display of enormous data sets as vectors in client-side map browsers by tiling the vectors into constant-size files.
Research Tools: code, datasets, & models
Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
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Viral Economy Simulator
Financial efficiency is the premier performance measure for most systems. Existing economic ecosystems for distribution of multimedia leave a lot to be desired: client-server platforms do not scale well, resulting in substantial operational costs, whereas…
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Switching Linear Dynamic Model
This code enables training and evaluation of a switching linear dynamic model for enhancing cepstral streams for automatic speech recognition, as described in our ICASSP 2004 paper, Noise Robust Speech Recognition with a Switching Linear…
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Digital Effects for MSN Messenger – 1.1.1033
Digital Effects, a plug-in for MSN Messenger video chatting, produces special effects for video communication. While the plug-in loads, users can select 3-D costumes or other digital effects in a pop-up window, and those effects…
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Orthant-Wise Limited-memory Quasi-Newton Optimizer for L1-regularized Objectives
The Orthant-Wise Limited-memory Quasi-Newton algorithm (OWL-QN) is a numerical optimization procedure for finding the optimum of an objective of the form {smooth function} plus {L1-norm of the parameters}. It has been used for training log-linear…
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Microsoft Research MapSynthesis
HTML and JavaScript Web code that displays a user interface to enable users to search, assimilate, and compare maps rapidly from an extensive corpus. The download includes a reference to a sample corpus of approximately…
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Impact of Controlled Language on Machine-Translation Quality and Post-Editing Efforts
Results from experiments conducted by Microsoft Research’s Machine Translation Incubation Team to investigate the impact of using good English (controlled language) on post-editing productivity—as well as on the overall quality of our statistical machine-translation system.