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Topic | Description | Read Time |
---|---|---|
BaseModel vs TIGER for sequential recommendations | The comparison between BaseModel and TIGER reveals substantial differences in their architectural choices and performance. | 8 min read |
BaseModel vs HSTU for sequential recommendations | To evaluate BaseModel against HSTU, we replicated the exact data preparation, training, validation, and testing protocols described in the HSTU paper. | 8 min read |
Fourier Feature Encoding of numerical features | Pre-processing raw input data is a very important part of any machine learning pipeline, often crucial for end model performance | 8 min read |
Why We Need Inhuman Artificial Intelligence | We continuously wonder how much longer it will take until AI reaches human skill level in these tasks - or, when does AI become "truly" intelligent. | 6 min read |
EMDE vs Multiresolution Hash Encoding | When we created our EMDE algorithm we primarily had in mind the domain of behavioral profiling. | 12 min read |
Efficient integer pair hashing | Mental models are simple expressions of complex processes or relationships. | 8 min read |
Cleora: how we handle billion-scale graph data | We have recently open sourced Cleora â an ultra fast vertex embedding tool for graphs & hypergraphs. | 9 min read |
Towards a multi-purpose behavioral model | In various subfields of AI research, there is a tendency to create models which can serve many different tasks with minimal fine-tuning effort. | 8 min read |
EMDE Illustrated | In this article we provide some intuitive explanations of our objectives and theoretical background of the Efficient Manifold Density Estimator (EMDE) | 10 min read |
How we challenge the Transformer | Having achieved remarkable successes in natural language and image processing, Transformers have finally found their way into the area of recommendation. | 7 min read |
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