kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search

• URL: http://arxiv.org/abs/2501.06121v1
• Date: Fri, 10 Jan 2025 17:19:59 GMT
• Title: kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search
• Authors: Leonardo Delfino, Domenico Erriquez, Silvio Martinico, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini,
• Abstract summary: Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. We introduce kANNolo, a novel research-oriented ANN library written in Rust and explicitly designed to combine usability with performance effectively.
• Score: 5.394883109521642
• License:
• Abstract: Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel research-oriented ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. This abstraction ensures flexibility and facilitates an easy integration of new components. In this work, we detail the architecture of kANNolo and demonstrate that its flexibility does not compromise performance. The experimental analysis shows that kANNolo achieves state-of-the-art performance in terms of speed-accuracy trade-off while allowing fast and easy prototyping, thus making kANNolo a valuable tool for advancing ANN research. Source code available on GitHub: https://github.com/TusKANNy/kannolo.

Related papers

• Benchmarking Predictive Coding Networks -- Made Simple [48.652114040426625]
  We tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. We propose a library, called PCX, that focuses on performance and simplicity, and use it to implement a large set of standard benchmarks. We perform extensive tests on such benchmarks using both existing algorithms for PCNs, as well as adaptations of other methods popular in the bio-plausible deep learning community.
  arXiv  Detail & Related papers  (2024-07-01T10:33:44Z)
• Towards Better Dynamic Graph Learning: New Architecture and Unified Library [29.625205125350313]
  DyGFormer is a Transformer-based architecture for dynamic graph learning. DyGLib is a unified library with standard training pipelines and coding interfaces.
  arXiv  Detail & Related papers  (2023-03-23T05:27:32Z)
• NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks [76.8112416450677]
  Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL) NASiam is a novel approach that uses for the first time differentiable NAS to improve the multilayer perceptron projector and predictor (encoder/predictor pair) NASiam reaches competitive performance in both small-scale (i.e., CIFAR-10/CIFAR-100) and large-scale (i.e., ImageNet) image classification datasets while costing only a few GPU hours.
  arXiv  Detail & Related papers  (2023-01-31T19:48:37Z)
• NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search [55.75621026447599]
  We propose NAS-Bench-Graph, a tailored benchmark that supports unified, reproducible, and efficient evaluations for GraphNAS. Specifically, we construct a unified, expressive yet compact search space, covering 26,206 unique graph neural network (GNN) architectures. Based on our proposed benchmark, the performance of GNN architectures can be directly obtained by a look-up table without any further computation.
  arXiv  Detail & Related papers  (2022-06-18T10:17:15Z)
• Sionna: An Open-Source Library for Next-Generation Physical Layer Research [64.77840557164266]
  Sionna is a GPU-accelerated open-source library for link-level simulations based on ray kernels. Sionna implements a wide breadth of carefully tested state-of-the-art algorithms that can be used for benchmarking and end-to-end performance evaluation.
  arXiv  Detail & Related papers  (2022-03-22T16:31:44Z)
• Efficient Person Search: An Anchor-Free Approach [86.45858994806471]
  Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id branch upon two-stage detectors like Faster R-CNN. In this work, we present an anchor-free approach to efficiently tackling this challenging task, by introducing the following dedicated designs.
  arXiv  Detail & Related papers  (2021-09-01T07:01:33Z)
• FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces [0.0]
  The functional neural network (FuncNN) library is the first such package in any programming language. This paper introduces functions that provide users an avenue to easily build models, generate predictions, and run cross-validations.
  arXiv  Detail & Related papers  (2020-09-18T22:32:29Z)
• Captum: A unified and generic model interpretability library for PyTorch [49.72749684393332]
  We introduce a novel, unified, open-source model interpretability library for PyTorch. The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms. It can be used for both classification and non-classification models.
  arXiv  Detail & Related papers  (2020-09-16T18:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.