• UMAP

  • Referenced in 37 articles [sw34900]
  • Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed...
  • MADMM

  • Referenced in 16 articles [sw38277]
  • smooth optimization on manifolds. Numerous problems in machine learning are formulated as optimization with manifold ... dimensionality reduction, data analysis, and manifold learning...
  • Manopt

  • Referenced in 125 articles [sw08493]
  • efficient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank ... Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor...
  • mdp

  • Referenced in 11 articles [sw14129]
  • collection of supervised and unsupervised learning algorithms and other data processing units that ... Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers...
  • Megaman

  • Referenced in 5 articles [sw17838]
  • Megaman: scalable manifold learning in python. Manifold Learning (ML) is a class of algorithms seeking ... manifold. Despite this, most existing manifold learning implementations are not particularly scalable. Here we present ... package that implements a variety of manifold learning algorithms in a modular and scalable fashion ... package incorporates theoretical advances in manifold learning, such as the unbiased Laplacian estimator introduced...
  • geomstats

  • Referenced in 11 articles [sw24373]
  • Machine Learning. We introduce geomstats, a python package that performs computations on manifolds such ... extensively unit-tested implementations of these manifolds, together with useful Riemannian metrics and associated Exponential ... implementation and integrated geomstats manifold computations into keras deep learning framework. This paper ... also presents a review of manifolds in machine learning and an overview of the geomstats...
  • McTorch

  • Referenced in 3 articles [sw38978]
  • McTorch Lib, a manifold optimization library for deep learning. McTorch is a Python library that ... manifold constrained tensors to address nonlinear optimization problems. Facilitates constrained weight tensors in deep learning...
  • COCO-GAN

  • Referenced in 3 articles [sw42459]
  • First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches...
  • HIGAN

  • Referenced in 1 article [sw43066]
  • sampling from a 100-dimension manifold, learned by the generator, that characterizes the fully...
  • SemiBoost

  • Referenced in 7 articles [sw43049]
  • performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient ... iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models ... performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples...
  • MagNet

  • Referenced in 5 articles [sw41285]
  • MagNet learns to differentiate between normal and adversarial examples by approximating the manifold of normal...
  • Manifold Regularization

  • Referenced in 1 article [sw24840]
  • Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. We propose...
  • GraphDemo

  • Referenced in 1 article [sw10148]
  • learning. Many machine learning algorithms model local neighborhoods using similarity graphs: manifold methods for dimensionality ... spectral clustering, label propagation for semi-supervised learning, and so on. However, for most...
  • SRFlow-DA

  • Referenced in 1 article [sw42559]
  • remarkable performance by learning an exact map-ping from HR image manifold to a latent ... allows sampling multiple output images from a learned SR space with a given LR image...
  • SphereFace

  • Referenced in 4 articles [sw39108]
  • that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss ... imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also...
  • ReliefE

  • Referenced in 1 article [sw42900]
  • spaces via manifold embeddings. Feature ranking has been widely adopted in machine learning applications such ... dimensional representations, potentially facilitating down-stream learning capabilities of conventional learners. This paper explores ... adapted to benefit from (Riemannian) manifold-based embeddings of instance and target spaces, where...
  • Isomap

  • Referenced in 12 articles [sw31686]
  • uses easily measured local metric information to learn the underlying global geometry of a data ... important class of data manifolds, is guaranteed to converge asymptotically to the true structure...
  • ADOL-C

  • Referenced in 257 articles [sw00019]
  • ADOL-C: Automatic Differentiation of C/C++. We present...
  • CGAL

  • Referenced in 403 articles [sw00118]
  • The goal of the CGAL Open Source Project...
  • CLIFFORD

  • Referenced in 85 articles [sw00131]
  • CLIFFORD performs various computations in Grass mann and...