• Neural Network Toolbox

  • Referenced in 178 articles [sw07378]
  • networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With...
  • node2vec

  • Referenced in 79 articles [sw27202]
  • nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional ... exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec...
  • Fuzzy ARTMAP

  • Referenced in 108 articles [sw42104]
  • network architecture for incremental supervised learning of analog multidimensional maps...
  • ANFIS

  • Referenced in 279 articles [sw08730]
  • hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both...
  • FaceNet

  • Referenced in 30 articles [sw21626]
  • system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean ... intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets...
  • LSTM

  • Referenced in 30 articles [sw03373]
  • applications: general computers which can learn algorithms to map input sequences to output sequences, with...
  • SVMstruct

  • Referenced in 31 articles [sw04075]
  • structured outputs. It performs supervised learning by approximating a mapping...
  • NICE

  • Referenced in 15 articles [sw29631]
  • deterministic transformation of the data is learned that maps it to a latent space ... trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition...
  • SegNet

  • Referenced in 27 articles [sw27575]
  • decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices ... eliminates the need for learning to upsample. The upsampled maps are sparse and are then...
  • SpectralNet

  • Referenced in 6 articles [sw26162]
  • network, which we call SpectralNet, learns a map that embeds input data points into ... network output orthogonal. Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding...
  • ML-KNN

  • Referenced in 74 articles [sw12923]
  • this paper, a multi-label lazy learning approach named ML-KNN is presented, which ... each possible class, maximum a posteriori (MAP) principle is utilized to determine the label ... three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene...
  • PersistenceImages

  • Referenced in 35 articles [sw41418]
  • made to map PDs into spaces with additional structure valuable to machine learning tasks ... with vector-based machine learning tools, such as linear sparse support vector machines, which identify ... discrete dynamical system (the linked twist map) and a partial differential equation (the anisotropic Kuramoto...
  • HapMix

  • Referenced in 6 articles [sw33847]
  • wide range of applications from disease mapping to learning about history. Most methods require ... explore its utility for inferring ancestry, learning about ancestral populations, and inferring dates of admixture ... HAPMIX will be of particular utility for mapping disease genes in recently admixed populations...
  • SuLQ

  • Referenced in 128 articles [sw11355]
  • database and f is a function mapping database rows to {0, 1}. The true answer ... operate in the in the statistical query learning model...
  • Emacs

  • Referenced in 15 articles [sw26546]
  • associated learning curve, you will appreciate the comprehensive appendix that maps vi commands to their...
  • VAMPnets

  • Referenced in 19 articles [sw32927]
  • Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks ... dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus...
  • Noise2Noise

  • Referenced in 2 articles [sw40722]
  • signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with ... powerful conclusion: it is possible to learn to restore images by only looking at corrupted...
  • ConvS2S

  • Referenced in 1 article [sw26536]
  • prevalent approach to sequence to sequence learning maps an input sequence to a variable length...
  • D2C

  • Referenced in 2 articles [sw14323]
  • using a classifier to learn a mapping between the features and the presence...
  • geomstats

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