Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...

Recent advancements in embedding models have focused on transforming general-purpose text representations for diverse applications like semantic similarity, clustering, and classification. Traditional ...

Google on Friday added a new, experimental “embedding” model for text, Gemini Embedding, to its Gemini developer API. Embedding models translate text inputs like words and phrases into numerical ...

Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, United States Center for Computation and Technology, Louisiana State University, Baton Rouge, ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...

When I train both autoencoder and embedding, I will get two ckpts. How do I use these two ckpts as ldm's ckpts? In ldm's config file, only one ckpt element is defined.

YouTube is testing serving ads directly embedded into video streams – the latest move in its war on ad-blockers. Why it matters. The “server-side ad injection” approach bakes ads into the core video ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

Abstract: In the task of multi-label classification, it is a key challenge to determine the correlation between labels. One solution to this is the Target Embedding Autoencoder (TEA), but most ...