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Found 10 papers, 1 papers with code
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Space
In this paper, we compare the performances of FAISS and FENSHSES on nearest neighbor search in Hamming space--a fundamental task with ubiquitous applications in nowadays eCommerce.
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines.
A Machine Learning Approach to Shipping Box Design
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers.
Towards Practical Visual Search Engine within Elasticsearch
In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine.
Revisiting Skip-Gram Negative Sampling Model with Rectification
We revisit skip-gram negative sampling (SGNS), one of the most popular neural-network based approaches to learning distributed word representation.
Discrete Graph Hashing
Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases.
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learning.
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
The most popular convex relaxation of this problem minimizes the sum of the nuclear norms of the unfoldings of the tensor.
Toward Guaranteed Illumination Models for Non-Convex Objects
Illumination variation remains a central challenge in object detection and recognition.
