diff --git a/neurips21.html b/neurips21.html index d2c6b4d9..e7bc65f3 100644 --- a/neurips21.html +++ b/neurips21.html @@ -277,7 +277,7 @@

Benchmark Datasets

1B points 100M base points 10K queries - link + link CC0 @@ -298,10 +298,10 @@

Benchmark Datasets

100 L2 k-NN - 1B points + 1B points N/A - 100K queries - link + 100K queries + link link to terms @@ -310,10 +310,10 @@

Benchmark Datasets

100 L2 k-NN - 1B points - 100M base points - 29.3K queries - link + 1B points + 100M base points + 29.3K queries + link O-UDA @@ -378,7 +378,7 @@

Metrics


- Baseline DiskANN indices for T2 can be downloaded using "azcopy copy 'https://comp21storage.blob.core.windows.net/publiccontainer/comp21/diskann-T2-baseline-indices' 'local_folder' --recursive". + Baseline DiskANN indices for T2 can be downloaded using "azcopy copy 'https://comp21storage.z5.web.core.windows.net/comp21/diskann-T2-baseline-indices' 'local_folder' --recursive". Note that this would take some time as the indices are large. All indices were built using R and L parameters set to 100. Search for T2 used 16 threads and beamwidth 4. The Ls parameter was varied to tune recall vs QPS.
Update: T2 baseline results have been modified after measuring via pybind11 interface on docker. There was a 30-40% QPS loss using this interface