An example implementation of a three-dimensional (3D) Vector-Quantized Variational Autoencoder (VQ-VAE) prototype, here used for the compression task of 3D data cubes. This 3D VQ-VAE is an extension ...
Imagine looking for similar things based on deeper insights instead of just keywords. That’s what vector databases and similarity searches help with. Vector databases enable vector similarity search.
Write a program to implement vector quantization on a gray-scale image using a "vector" that consists of a 4x4 block of pixels. Design your codebook using all the blocks in the image as training data, ...
Abstract: Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and ...
Abstract: Vector quantization (VQ), which treats a vector as a compression unit, gains increasing research interests for its potential to accelerate large language models (LLMs). Compared to ...
New capabilities deliver up to 5X faster filtered vector search, improved ranking quality, and lower infrastructure costs to unlock scalable, cost-efficient AI applications SAN FRANCISCO, July 30, ...
Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushing weight-only ...
Autoregressive image generation models have traditionally relied on vector-quantized representations, which introduce several significant challenges. The process of vector quantization is ...
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