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Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Nov 7, 2024 · Artificial Intelligence

RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search

RTAMS‑GANNS, the award‑winning real‑time adaptive multi‑stream GPU system for online approximate nearest neighbor search, eliminates costly memory allocations and serial execution by using a dynamic memory‑block insertion algorithm and separate CUDA streams, cutting latency by 40‑80% and reliably serving over 100 million daily users in production.

GPUMulti‑StreamOnline Search
0 likes · 19 min read
RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search
Baidu Tech Salon
Baidu Tech Salon
Sep 25, 2023 · Artificial Intelligence

Approximate Nearest Neighbor Retrieval and Baidu's Open‑Source Puck Framework – Event Overview

Baidu’s upcoming technical salon series on search will showcase its open‑source ANN retrieval framework Puck—demonstrating superior performance on massive benchmarks—and feature nightly live sessions covering product management, GPU optimization, intelligent Q&A, model architecture, and image generation, with registration via QR‑code.

AIBaiduOpen-source
0 likes · 2 min read
Approximate Nearest Neighbor Retrieval and Baidu's Open‑Source Puck Framework – Event Overview
DataFunSummit
DataFunSummit
Aug 3, 2023 · Artificial Intelligence

Integrating Vector Databases with Large Language Models for Enterprise AI Applications

The article explains how combining vector databases with large language models can help governments and enterprises leverage massive private data for AI, covering semantic search, approximate nearest neighbor techniques, alignment challenges across modalities, and future directions for fine‑grained data integration.

AISemantic Searchapproximate nearest neighbor
0 likes · 7 min read
Integrating Vector Databases with Large Language Models for Enterprise AI Applications
Kuaishou Tech
Kuaishou Tech
Apr 24, 2023 · Artificial Intelligence

Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation

This paper identifies the trade‑off between simple and hard negatives in embedding‑based retrieval for recommendation, proposes a clustering‑based divide‑and‑conquer framework combined with prompt‑driven multi‑task learning to improve relevance, diversity, and fairness, and validates the approach through offline metrics, online A/B tests, and comparative experiments.

Prompt TuningRecommendation systemsapproximate nearest neighbor
0 likes · 9 min read
Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Jan 17, 2022 · Artificial Intelligence

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

This article introduces the concept of vector retrieval, outlines its diverse application scenarios, explains common distance metrics for both floating‑point and binary vectors, and surveys fundamental approximate nearest‑neighbor algorithms including tree‑based, graph‑based, quantization, and hashing methods.

HNSWKD-TreeLSH
0 likes · 22 min read
Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms
DataFunSummit
DataFunSummit
Jan 10, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms raw physical‑world data into dense vectors, defines the significance of vector retrieval, surveys common use cases such as image, video, and text search, discusses challenges in representation learning, and reviews high‑performance approximate nearest‑neighbor algorithms and practical deployments.

AI applicationsVector Retrievalapproximate nearest neighbor
0 likes · 21 min read
Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms
Laiye Technology Team
Laiye Technology Team
Jan 7, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms unstructured data into dense vectors, defines vector retrieval, outlines its many use cases such as product, video, and text search, discusses challenges in learning effective embeddings, and reviews high‑performance algorithms like LSH, neighbor graphs, and product quantization.

AI applicationsHNSWLSH
0 likes · 21 min read
Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms
iQIYI Technical Product Team
iQIYI Technical Product Team
Feb 21, 2020 · Artificial Intelligence

Top-1 Solution for the 2019 CCF Big Data & Computing Intelligence Competition: Video Copyright Detection

The Hengyang Data team won the 2019 CCF Big Data & Computing Intelligence video‑copyright detection contest by extracting VGG16‑based image features with Gaussian‑R‑MAC weighting, using a graph‑based NSG nearest‑neighbor search and a frame‑matching algorithm to locate infringing segments within three‑second precision, even under severe cropping and other transformations.

CCF competitionVGG16Video Copyright Detection
0 likes · 9 min read
Top-1 Solution for the 2019 CCF Big Data & Computing Intelligence Competition: Video Copyright Detection