Granica Named a 2023 Gartner® Cool Vendor

Pioneering fundamental research into AI efficiency.

We believe that by increasing the information density of data we can help make AI more impactful and useful for everyone.

“Fundamental research is at the core of what we do at Granica and we've combined that with our team's systems engineering expertise to build a platform that drives information density and efficiency, at cloud scale.”

Andrea Montanari, Chief Scientist at Granica

Featured Research

Sep 25, 2023
Towards a statistical theory of data selection under weak supervisionGiven a sample of size N, it is often useful to select a subsample of smaller size n<N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning.
Information DistillationRead paper
May 18, 2023
Sampling, Diffusion, and Stochastic LocalizationDiffusions are a successful technique to sample from high-dimensional distributions that are not given explicitly but rather learnt from a collection of samples. We generalize the construction of stochastic localization processes.
AlgorithmsRead paper
Feb 20, 2023
Compressing Tabular Data via Latent Variable Estimation (ICML 2023)Data used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data.
Tabular CompressionRead paper
Sept 14, 2021
Inline Data Detection in Large Data StreamsWe present a a novel approach to data processing and reduction method that involves receiving an input data stream and computing a set of features that are representative of or unique to the stream.
Lossless Data ReductionRead paper
Mar 2, 2021
Efficient Data Deduplication through Sketch Computation and Similarity MetricsThe methods provide a more efficient and effective way of handling large data streams, which can be particularly beneficial in applications that require real-time data processing and reduction.
Lossless Data ReductionRead paper

Let's build the future of AI efficiency, together.

A diversity of products requires a diversity of perspectives. We're on a mission to build tools that are easy to use and impossible to ignore — and we want your unique voice.

Whether you're a researcher, a systems engineer, an ML engineer, or simply someone interested in a more efficient future for AI, reach out.

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