Using predefined vector systems to speed up neural network multimillion class classification
Label prediction in neural networks (NNs) has O(n) complexity proportional to the number of classes. This holds true for classification using fully connected layers and cosine similarity with some set of class prototypes. In this paper we show that if NN latent space (LS) geometry is known and possesses specific properties, label prediction complexity can be significantly reduced. This is achieved by associating label prediction with the O(1) complexity closest cluster center search in a vector system used as target for latent space configuration (LSC). The proposed method only requires finding indexes of several largest and lowest values in the embedding vector making it extremely comput...
| Authors | Nikita Gabdullin, Ilya Androsov |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 6 April 2026 |
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