Papers for

recommendation engine developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Latent space tweaks speed up neural network search and ranking

Latent space configuration for neural network finetuning, fast ranking, and embedding dimensionality reduction

Abstract: Finding K most similar elements for a query in a large gallery of neural network (NN) embeddings is essential in various areas of computer science and industrial applications of artificial intelligence. This paper proposes search algorithms for structured latent spaces (LS) of NNs which allow to speed up top K item retrieval owing to O(1) complexity small candidate gallery subset search. ArcFace-pretrained IResNet50 models are finetuned using Latent Space Configuration (LSC) methodology to obtain new distribution of embeddings with desired geometric properties. Applying the proposed algorithms to embeddings obtained from finetuned models leads to several times speed-up over baseline exhaustive search. Specifically, we report up to 3 times speed-up on GPU and 40% speed-up on CPU compared to the baseline method GPU speed. These results are obtained for the original and reduced numbers of LS dimensions. The results show that LS dimensionality reduction allows to further accelerate the search and even improve rank-N metrics.

Wed 21 OctComputer Vision and Pattern RecognitionArtificial Intelligence
The gist
Finding the most similar items in a big set of neural network outputs is often slow. The authors show a way to adjust how these outputs are arranged to make finding matches much faster without losing accuracy. They improve a common model with a new method that reshapes these outputs and speeds up searching by up to three times on GPUs. They also show that shrinking the size of these outputs can make the search even quicker and sometimes better for ranking. This can help any system relying on comparing many neural network-generated features.
Open → 2610.29295v1