Vienna startup Ora Computing raised €3.5M and proved a 70-billion-parameter large language model can be compressed for under ...

Abstract: In this paper, we consider the problem of blindly calibrating a mobile sensor network-i.e., determining the gain and the offset of each sensor-from heterogeneous observations on a defined ...

With the growing use of multiple social platforms, aligning user identities across networks, known as Social Network Alignment (SNA), has become ...

Google published a research paper about helping recommender systems understand what users mean when they interact with them. Their goal with this new approach is to overcome the limitations inherent ...

Note: Nonnegative Matrix Factorization is an area of active research. New algorithms are proposed every year. Contributions are very welcomed. Most types and functions (except the high-level function ...

Whether we’re using Spotify, Amazon, Netflix or Instagram, we encounter algorithms that recommend content or products to us every day. In 2017 Netflix stated that its users discover around 80 percent ...

Abstract: Nonnegative matrix factorization (NMF) is a widely used data analysis technique and has yielded impressive results in many real-world tasks. Generally, existing NMF methods represent each ...

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible ...