Combined with hypergraph convolution and Fourier KAN techniques, NCRAE achieves effective node embedding learning. Experiments on cancer-related ceRNA data sets show that NCRAE outperforms existing ...

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 TEA-based ...

French startup Mistral AI on Wednesday unveiled Codestral Embed, its first code-specific embedding model, claiming it outperforms rival offerings from OpenAI, Cohere, and Voyage.

Mistral's Codestral Embed will help make RAG use cases faster and find duplicate code segments using natural language.

RCSB Embedding Model is a neural network architecture designed to encode macromolecular 3D structures into fixed-length vector embeddings for efficient large-scale structure similarity search.

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ...

The diagram above illustrates the framework of TransformCode for unsupervised learning of code embedding using contrastive learning. It consists of two main phases: Before Training and Contrastive ...

Autoencoder for Product Matching This was an experiment for a possible PhD topic. The main idea was to use different Autoencoder for entity resolution / product matching. The core idea was to pretrain ...