1 code implementation • 11 Dec 2023 • Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
no code implementations • 24 Jun 2019 • Cun Mu, Binwei Yang, Zheng Yan
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.
no code implementations • 20 Feb 2019 • Cun Mu, Jun Zhao, Guang Yang, Binwei Yang, Zheng Yan
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.
no code implementations • 26 Sep 2018 • Guang Yang, Cun Mu
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.
no code implementations • 23 Jun 2018 • Cun Mu, Jun Zhao, Guang Yang, Jing Zhang, Zheng Yan
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.
no code implementations • 1 Apr 2018 • Cun Mu, Guang Yang, Zheng Yan
We revisit skip-gram negative sampling (SGNS), one of the most popular neural-network based approaches to learning distributed word representation.
no code implementations • NeurIPS 2014 • Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases.
no code implementations • 29 Mar 2014 • Cun Mu, Yuqian Zhang, John Wright, Donald Goldfarb
Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learning.
no code implementations • 22 Jul 2013 • Cun Mu, Bo Huang, John Wright, Donald Goldfarb
The most popular convex relaxation of this problem minimizes the sum of the nuclear norms of the unfoldings of the tensor.
no code implementations • 4 Jul 2013 • Yuqian Zhang, Cun Mu, Han-Wen Kuo, John Wright
Illumination variation remains a central challenge in object detection and recognition.