turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...
As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...
Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning. This is what happens when you drink beer every day, according to experts DOJ aims to ...
Fixed-Dimensional Encoding (FDE) solves a fundamental problem in modern search systems: how to efficiently search through billions of documents when each document is represented by hundreds of vectors ...
ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle the easiest pieces first. But this kind of sorting has a cost.
In this tutorial, we demonstrate how to use the UAgents framework to build a lightweight, event-driven AI agent architecture on top of Google’s Gemini API. We’ll start by applying nest_asyncio to ...
Abstract: This paper deals with genetic algorithm implementation in Python. Genetic algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics. In ...
In this tutorial, we will discover how to harness the power of an advanced AI Agent, augmented with both Python execution and result-validation capabilities, to tackle complex computational tasks. By ...
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