Beyond Market Intelligence/similarity scores

similarity scores

similarity scores on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on similarity scores in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around similarity scores, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

SSOG-Attention: Sum Of Separable Gaussians as a sub-quadratic and scalable alternative to SDPA. [R]
Machine Learning

SSOG-Attention: Sum Of Separable Gaussians as a sub-quadratic and scalable alternative to SDPA. [R]

Scaled dot-product attention (SDPA) faces a significant scalability bottleneck, exhibiting O(N²·d) complexity. A new approach, Sum Of Separable Gaussians (SSOG), offers a compelling alternative. SSOG learns a few Gaussian atoms per head, geometrically steering them for efficient computation—achieving a reduced complexity of O(N·√N·d). Experiments demonstrate SSOG’s superiority on smaller datasets like CIFAR100 and equivalent, faster convergence on larger datasets like IN1k, while maintaining memory efficiency. Explore the full details and results in the blog post and repository.

Comparing embedding models with synthetic query probing [R]
Machine Learning

Comparing embedding models with synthetic query probing [R]

Evaluating different embedding models—like transitioning from ADA to Titan—can be surprisingly complex. Direct comparison of embedding spaces isn't inherently possible, so how do you determine equivalency or establish useful thresholds for retrieval? Our research addresses this with Synthetic Query Probing, a straightforward method that compares similarity spaces instead. By analyzing similarity scores across models for paired content, we reveal non-linear relationships and varying ranges, as illustrated in our recent paper.