• MMD GAN

  • Referenced in 12 articles [sw42580]
  • computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian ... name it MMD GAN. The new distance measure in MMD GAN is a meaningful loss...
  • CDNA

  • Referenced in 9 articles [sw37103]
  • noted. That is, matches of Hamming distance 0,1,2, etc. Information from each ... parameters of the combining tree are learned using EM (Expectation Maximization). Our focus in Significantly...
  • NETT

  • Referenced in 18 articles [sw41773]
  • there are few theoretical results for deep learning in inverse problems. In this paper ... framework based on the absolute Bregman distance generalizing the standard Bregman distance from the convex...
  • PseDNA-Pro

  • Referenced in 6 articles [sw22424]
  • Combining Chou’s PseAAC and Physicochemical Distance Transformation. Identification of DNA-binding proteins ... various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance ... composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider...
  • BAYES-NEAREST

  • Referenced in 4 articles [sw02847]
  • Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure ... data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used...
  • SMOQ

  • Referenced in 1 article [sw20299]
  • developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue...
  • ProMP

  • Referenced in 2 articles [sw34914]
  • gained insights we develop a novel meta-learning algorithm that overcomes both the issue ... meta-policy gradients. By controlling the statistical distance of both pre-adaptation and adapted policies ... proposed algorithm endows efficient and stable meta-learning. Our approach leads to superior pre-adaptation...
  • ArcFace

  • Referenced in 5 articles [sw33958]
  • main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face ... enhance discriminative power. Centre loss penalises the distance between the deep features and their corresponding...
  • Mind2Mind

  • Referenced in 1 article [sw42585]
  • optimal transport, ensuring the convergence of the learning of the transferred GAN. We moreover provide ... training in terms of the distance between the source and target dataset...
  • PolarFormer

  • Referenced in 1 article [sw42475]
  • Polar’s distance dimension, we further introduce a multi-scalePolar representation learning strategy...
  • Kaolin

  • Referenced in 2 articles [sw31201]
  • differentiable 3D modules for use in deep learning systems. With functionality to load and preprocess ... native functions to manipulate meshes, pointclouds, signed distance functions, and voxel grids, Kaolin mitigates ... many state-of-the-art 3D deep learning architectures, to serve as a starting point...
  • DLCAnalyzer

  • Referenced in 1 article [sw39569]
  • visits, distance moved etc. but can also be integrated with supervised machine learning and unsupervised...
  • Foolbox

  • Referenced in 3 articles [sw20935]
  • Additionally, Foolbox interfaces with most popular deep learning frameworks such as PyTorch, Keras, TensorFlow, Theano ... misclassification as well as different distance measures. The code is licensed under the MIT license...
  • ASSAM

  • Referenced in 6 articles [sw13842]
  • semantic metadata are supported by two machine learning algorithms. First, we have developed an iterative ... based on an ensemble of string distance metrics...
  • BayesMallows

  • Referenced in 1 article [sw26332]
  • Machine Learning Research, 2018 ). Both Cayley, footrule, Kendall, and Spearman distances...
  • IGLUE

  • Referenced in 4 articles [sw31671]
  • complexity and accuracy of lattice-based learning systems. For this purpose, IGLUE uses the entropy ... nearest neighbor technique with the Mahalanobis distance as the similarity measure between redescribed instances. IGLUE ... measures highlight the importance of instance-based learning through lattice theory...
  • GraphRNN

  • Referenced in 1 article [sw36060]
  • with minimal assumptions about their structure. GraphRNN learns to generate graphs by training ... distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning...
  • SphereFace

  • Referenced in 4 articles [sw39108]
  • intra-class distance than minimal inter-class distance under a suitably chosen metric space. However ... that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss...
  • pybeach

  • Referenced in 1 article [sw31604]
  • transects. It includes the following methods: Machine learning; Maximum curvature (Stockdon et al, 2007); Relative ... relief (Wernette et al, 2016); and, Perpendicular distance. In addition, pybeach contains methods for identifying...
  • LS-MCMC

  • Referenced in 2 articles [sw41466]
  • algorithm has achieved great success in Bayesian learning and posterior sampling. However, SGLD typically suffers ... strictly smaller discretization error in 2-Wasserstein distance, although its mixing rate can be slightly ... performance of LS-SGLD on different machine learning tasks including posterior sampling, Bayesian logistic regression...