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If someone is looking for the referenced paper (Accelerating Large-Scale Inference with Anisotropic Vector Quantization), it can be found here: https://arxiv.or
by greenyoda 6y ago
If someone is looking for the referenced paper (Accelerating Large-Scale Inference with Anisotropic Vector Quantization), it can be found here: https://arxiv.org/abs/1908.10396 https://arxiv.org/abs/1908.10396
(It's also linked in the repository's docs/algorithms.md, to which there's a link at the bottom of the main page.)
- thomasahle 6y agoThe paper is a bit odd. It feels like a lot of things are missing: - How are the quantized vectors used to do NNS? Do we have to brute force compare against each codeword? - In thst case, how many codewords are there? How big is k compared to n? How does changing k affect performance? - What architecture is used to train the embeddings? We are only given the loss function. - How fast is the preprocessing step? If we need to do Lloyd's algorithm type iterations combined with training neural nets, this is potentially a lot slower than Faiss which just picks random landmark points.