Random Projections with Asymmetric Quantization
–Neural Information Processing Systems
The method of random projection has been a popular tool for data compression, similarity search, and machine learning. In many practical scenarios, applying quantization on randomly projected data could be very helpful to further reduce storage cost and facilitate more efficient retrievals, while only suffering from little loss in accuracy.
Neural Information Processing Systems
Oct-3-2025, 09:27:55 GMT
- Country:
- North America
- United States
- District of Columbia > Washington (0.04)
- Texas > Dallas County
- Dallas (0.04)
- New York > Kings County
- New York City (0.04)
- New Jersey
- Middlesex County > Piscataway (0.04)
- Mercer County > Princeton (0.04)
- Hawaii > Honolulu County
- Honolulu (0.04)
- Florida > Broward County
- Fort Lauderdale (0.04)
- California
- San Francisco County > San Francisco (0.14)
- Santa Clara County > Stanford (0.04)
- San Diego County > San Diego (0.04)
- Los Angeles County > Long Beach (0.04)
- Canada
- Quebec > Montreal (0.04)
- British Columbia > Vancouver Island
- Capital Regional District > Victoria (0.04)
- United States
- Europe
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Spain > Andalusia
- Cádiz Province > Cadiz (0.04)
- United Kingdom > England
- Asia
- China > Hong Kong (0.04)
- Afghanistan > Parwan Province
- Charikar (0.04)
- North America
- Technology: