• DistAl

  • Referenced in 103 articles [sw01746]
  • DistAl: An inter-pattern distance-based constructive learning algorithm Multi-layer networks of threshold logic ... constructive neural network learning algorithm (DistAl) based on inter-pattern distance is introduced. DistAl constructs ... distances of the training patterns. This offers a significant advantage over other constructive learning algorithms...
  • DeepSDF

  • Referenced in 6 articles [sw31206]
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. Computer graphics, 3D computer vision ... work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class ... point in the field represents the distance to the surface boundary and the sign indicates ... zero-level-set of the learned function while explicitly representing the classification of space...
  • pyDML

  • Referenced in 3 articles [sw35421]
  • pyDML: A Python Library for Distance Metric Learning. pyDML is an open-source python library ... that provides a wide range of distance metric learning algorithms. Distance metric learning...
  • FaceNet

  • Referenced in 30 articles [sw21626]
  • directly learns a mapping from face images to a compact Euclidean space where distances directly ... intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets...
  • dml

  • Referenced in 3 articles [sw20804]
  • package dml: Distance Metric Learning in R. The state-of-the-art algorithms for distance ... metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis ... Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature...
  • flexclust

  • Referenced in 27 articles [sw04583]
  • arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural...
  • TagProp

  • Referenced in 17 articles [sw11682]
  • TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation. Image auto-annotation ... neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing...
  • Persistence Landscape

  • Referenced in 18 articles [sw21260]
  • machine learning. We give efficient algorithms for calculating persistence landscapes, their averages, and distances between ... facilitate the combination of statistics and machine learning with topological data analysis. We present...
  • GANomaly

  • Referenced in 5 articles [sw41240]
  • conditional generative adversarial network that jointly learns the generation of high-dimensional image space ... distance between these images and the latent vectors during training aids in learning the data ... result, a larger distance metric from this learned data distribution at inference time is indicative...
  • metric-learn

  • Referenced in 3 articles [sw35420]
  • package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn...
  • FLAME

  • Referenced in 3 articles [sw36879]
  • Fast Large-scale Almost Matching Exactly), learns a distance metric for matching using a hold...
  • geomstats

  • Referenced in 11 articles [sw24373]
  • Python Package for Riemannian Geometry in Machine Learning. We introduce geomstats, a python package that ... corresponding geodesic distances provide a range of intuitive choices of Machine Learning loss functions...
  • GSML

  • Referenced in 4 articles [sw35600]
  • which we simultaneously learn both a good distance metric and a low-dimensional representation. Unfortunately...
  • PerMallows

  • Referenced in 16 articles [sw14542]
  • Mallows and Generalized Mallows Models. The considered distances are Kendall’s-tau, Cayley, Hamming ... includes functions for making inference, sampling and learning such distributions, some of which are novel ... Kendall’s-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random...
  • MWMOTE

  • Referenced in 14 articles [sw32596]
  • learn informative minority class samples and assigns them weights according to their euclidean distance from...
  • I-PETER

  • Referenced in 1 article [sw02387]
  • been designed for the on-line distance learning of Englishwhere too many students restrict...
  • quanteda

  • Referenced in 9 articles [sw30853]
  • similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text...
  • DOTmark

  • Referenced in 7 articles [sw17085]
  • mover’s distance (EMD) is a useful tool in statistics, machine learning and computer science ... increasingly complex data, the computation of these distances via optimal transport is often the limiting...
  • DistilBERT

  • Referenced in 10 articles [sw30758]
  • faster. To leverage the inductive biases learned by larger models during pre-training, we introduce ... loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model...
  • Biotope

  • Referenced in 2 articles [sw18796]
  • have been employed for agent learning, while the dispersal distance theory has been adopted...