A simpler and better LSH for Maximum Inner Product Search (MIPS)

By Behnam Neyshabur, Nati Srebro. 2014

5 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Retrieval & Search

1. Anshumali Shrivastava
2. Sanjiv Kumar
3. Ping Li
4. Ruiqi Guo
5. David Simcha
6. Krzysztof Marcin Choromanski
7. Nati Srebro
8. Behnam Neyshabur

In Optimization & Training

1. Ping Li
2. Sanjiv Kumar
3. Krzysztof Marcin Choromanski
4. David Simcha
5. Ruiqi Guo
6. Anshumali Shrivastava

Citations in the corpus

• Quantization based Fast Inner Product Search (2015, methodology): “They used a transformation similar to the one used by Bachrach et al. [2] to augment the original vectors: where ˜ x = x max x ∈ X || x || , ˜ q = q || q || .”
• Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, neutral): “We are aware another work [18] which performs very similarly to Sign-ALSH.”
• Asymmetric Minwise Hashing (2014, methodology): “Note that here we use ρ Sign derived from [3, 31] instead of ρ Sign ALSH for convenience although the two schemes perform essentially identically.”
• Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, methodology): “Note that here we use ρSign based on the slightly more convenient transformation from [3, 32] instead of ρSign−ALSH for convenience although the two schemes perform essentially the same.”