• DGM

  • Referenced in 152 articles [sw39282]
  • deep learning algorithm for solving partial differential equations. High-dimensional PDEs have been a longstanding ... high-dimensional Hamilton-Jacobi-Bellman PDE and Burgers’ equation. The deep learning algorithm approximates ... which can be viewed as a high-dimensional space). We call the algorithm a “Deep...
  • hglasso

  • Referenced in 11 articles [sw11202]
  • consider the problem of learning a high-dimensional graphical model in which there ... order to learn a sparse graph in the high-dimensional setting. However...
  • GP-COACH

  • Referenced in 10 articles [sw09137]
  • based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems ... Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule ... based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated...
  • camel

  • Referenced in 10 articles [sw14318]
  • implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT...
  • NICE

  • Referenced in 14 articles [sw29631]
  • propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent ... linear deterministic transformation of the data is learned that maps it to a latent space...
  • NeuralPDE.jl

  • Referenced in 33 articles [sw39548]
  • partial differential equations using scientific machine learning (SciML) techniques such as physics-informed neural networks ... neural stochastic differential equations to solve high-dimensional PDEs at a greatly reduced cost...
  • Celer

  • Referenced in 5 articles [sw37123]
  • inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems...
  • CHIME

  • Referenced in 9 articles [sw28514]
  • high-dimensional Gaussian mixtures with EM algorithm and its optimality. Unsupervised learning is an important ... learning with a wide range of applications. In this paper, we study clustering of high ... dimensional Gaussian mixtures and propose a procedure, called CHIME, that is based...
  • CORe50

  • Referenced in 4 articles [sw37988]
  • Continuous Object Recognition. Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem ... recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks...
  • FASTA

  • Referenced in 13 articles [sw37234]
  • machine learning, signal and image processing, communications, and beyond. For high-dimensional minimization problems involving...
  • bartMachine

  • Referenced in 10 articles [sw10962]
  • package bartMachine: Machine learning with Bayesian additive regression trees. We present a new package ... handling both large sample sizes and high-dimensional data...
  • GANomaly

  • Referenced in 5 articles [sw41240]
  • adversarial network that jointly learns the generation of high-dimensional image space and the inference ... latent vectors during training aids in learning the data distribution for the normal samples...
  • LWPR

  • Referenced in 7 articles [sw13543]
  • LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data...
  • spectralGraphTopology

  • Referenced in 1 article [sw35465]
  • data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become ... prominent task in machine learning and has found applications in many fields such as finance...
  • ZOOpt

  • Referenced in 2 articles [sw22396]
  • optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox ... tools in real-world machine learning tasks...
  • MetaGrad

  • Referenced in 1 article [sw40373]
  • full covariance matrix and is applicable to learning tasks for which we can afford update ... versions provide speed-ups for high-dimensional learning tasks with an update time that...
  • datamicroarray

  • Referenced in 6 articles [sw25719]
  • load small-sample, high-dimensional microarray data sets to assess machine learning algorithms and models...
  • BART-BMA

  • Referenced in 5 articles [sw23498]
  • considered a Bayesian version of machine learning tree ensemble methods where the individual trees ... popular for high-dimensional data is random forests, a machine learning algorithm which grows trees ... based algorithm which can deal with high-dimensional data. We have found that BART...
  • BaRC

  • Referenced in 3 articles [sw40037]
  • attractive approach to learn control policies for high-dimensional systems, but its relatively poor sample ... amount of exploration required to obtain a learning signal from the initial state...
  • ASKIT

  • Referenced in 11 articles [sw17470]
  • ASKIT: an efficient, parallel library for high-dimensional kernel summations. Kernel-based methods ... powerful tool in a variety of machine learning and computational statistics methods. A key bottleneck...