In data science, we sometimes need to calculate hash functions of unsigned 32-bit integer tuples. This need can arise in simple use cases such as efficient pairs counting, but also in approximate data structures which require fast hashing. Such data structures provide a tradeoff between accuracy and speed/memory storage:

Having achieved remarkable successes in natural language and image processing, Transformers have finally found their way into the area of recommendation. Recently, researchers from NVIDIA and Facebook AI joined forces to introduce Transformer-based recommendation models described in their RecSys2021 publication Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation,

EMDE – motivations, explanations and intuitions In this article we provide some intuitive explanations of our objectives and theoretical background of the Efficient Manifold Density Estimator (EMDE). We describe our inspirations and thought process which led to the formulation of the algorithm. Then, we dive further into technicalities to show how

In various subfields of AI research, there is a tendency to create models which can serve many different tasks with minimal fine-tuning effort. Stanford researchers introduced the concept of foundation models, which are trained on massive datasets and can be adapted to different downstream applications. We may think this way

We have recently open sourced Cleora — an ultra fast vertex embedding tool for graphs & hypergraphs [https://github.com/synerise/cleora]. Cleora can ingest any categorical, relational data and turn it into vector embeddings of entities. It is extremely fast, while offering very competitive quality of results. In fact, for