Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment
By Anshumali Shrivastava, Ping Li. 2015
86 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Retrieval & Search, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Computer Vision and Pattern Recognition
- Anshumali Shrivastava
- Ping Li
- Behnam Neyshabur
- Nati Srebro
- Sanjiv Kumar
- Yoshua Bengio
- Minjoon Seo
- Pascal Vincent
In Information Retrieval & Search
- Anshumali Shrivastava
- Ping Li
- Behnam Neyshabur
- Nati Srebro
- Sanjiv Kumar
- Minjoon Seo
- Ruiqi Guo
- Vidhisha Balachandran
In Signal Processing
- Anshumali Shrivastava
- Ping Li
- Sanjiv Kumar
- Vidhisha Balachandran
- Ruiqi Guo
- Nati Srebro
- Behnam Neyshabur
- Minjoon Seo
Citations in the corpus
- Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “We showed that for binary domain asymmetric minwise hashing is both empirically and provably superior, please see [24] for more details.”
- Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS) (2014, methodology): “We showed that for binary domain asymmetric minwise hashing is both empirically and provably superior, please see [24] for more details.”