• VAMPnets

  • Referenced in 19 articles [sw32927]
  • learning of molecular kinetics. There is an increasing demand for computing the relevant structures, equilibria ... simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation ... model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount ... Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks...
  • MIxBN

  • Referenced in 1 article [sw39155]
  • algorithm that allows structural learning and parameters learning from mixed data without discretization since data ... mixed MI score function for structural learning, and also linear regression and Gaussian distribution approximation ... Bayesian network on discretized data, (2) structural and parameters learning of a Bayesian network ... launching learning algorithms on one of two algorithms for enumerating graph structures - Hill-Climbing...
  • BENEDICT

  • Referenced in 13 articles [sw35868]
  • learning belief networks: BENEDICT. Previous algorithms for the construction of belief networks structures from data...
  • DirectLiNGAM

  • Referenced in 23 articles [sw15504]
  • Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. Structural equation models ... models are typically used to model the data-generating process of variables. Recently...
  • PersistenceImages

  • Referenced in 40 articles [sw41418]
  • tools from topological data analysis can characterize this structure for the purpose of knowledge discovery ... into spaces with additional structure valuable to machine learning tasks. We convert...
  • VFML

  • Referenced in 6 articles [sw11967]
  • data, to gather sufficient statistics from it, ADTs for several important machine learning structures ... series of tools for working with data sets: cleaning them, sampling them, splitting them into ... VFML contains tools for learning decision trees, for learning the structure belief nets (aka Bayesian ... scale to learning from very large data sets or from data streams...
  • BAYES-NEAREST

  • Referenced in 4 articles [sw02847]
  • obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm ... Network in the deduction phase. For those data bases in which some variables are continuous...
  • PyG

  • Referenced in 4 articles [sw41050]
  • related to structured data. It consists of various methods for deep learning on graphs...
  • TPMSVM

  • Referenced in 15 articles [sw12692]
  • many cases, especially when the data has heteroscedastic error structure, that is, the noise strongly ... there is an advantage in the learning speed compared with...
  • CMAR

  • Referenced in 53 articles [sw28406]
  • accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge ... efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules ... databases from the UCI machine learning database repository show that CMAR is consistent, highly effective...
  • CTBN-RLE

  • Referenced in 6 articles [sw12961]
  • implements structure and parameter learning for both complete and partial data. For inference, it implements...
  • CausalKinetiX

  • Referenced in 4 articles [sw37630]
  • stable and predictive structures in kinetic systems. Learning kinetic systems from data ... data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from ... simulated and real-world examples suggest that learning the structure of kinetic systems benefits from...
  • BiDAG

  • Referenced in 3 articles [sw31037]
  • Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data ... employs a hybrid approach, combining constraint-based learning with search and score. A reduced search ... continuous data and the BDe score is implemented for binary data or categorical data ... distribution given the data. All algorithms are also applicable to structure learning of dynamic Bayesian...
  • tree-structured-covariance

  • Referenced in 3 articles [sw23327]
  • Taxonomic prediction with tree-structured covariances. Taxonomies have been proposed numerous times in the literature ... learning, as similarities between classes can be used to increase the amount of relevant data ... show how data-derived taxonomies may be used in a structured prediction framework, and compare ... structured prediction with taxonomies; (iii) We show that the taxonomies learned from data using...
  • Eureka!

  • Referenced in 2 articles [sw00252]
  • hypothesize meaningful structures in the data, and a classification machine learning algorithm, to validate...
  • PEBL

  • Referenced in 1 article [sw14442]
  • Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data. In this paper ... library and application for learning Bayesian network structure from data and prior knowledge that provides...
  • tcherry

  • Referenced in 1 article [sw29491]
  • learning the structure of the type of graphical models called t-cherry trees from data ... structure is determined either directly from data or by increasing the order of a lower ... learning the structure of the type of graphical models called t-cherry trees from data ... provide functions for learning the structure of the graph from given data with no missing...
  • TensorNetwork

  • Referenced in 5 articles [sw38113]
  • TensorNetwork: A Library for Physics and Machine Learning. TensorNetwork is an open source library ... network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body ... number of other research areas, including machine learning. We demonstrate...
  • NeuroVectorizer

  • Referenced in 1 article [sw32381]
  • capture different instructions, dependencies, and data structures to enable learning a sophisticated model that...
  • Rdkit

  • Referenced in 10 articles [sw22741]
  • cheminformatics. A collection of cheminformatics and machine-learning software written in C++ and Python. NOTE ... github.com/rdkit/rdkit. The core algorithms and data structures are written in C++. Wrappers are provided...