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Logo JMLR GPstuff 4.5

by avehtari - July 22, 2014, 14:03:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12087 views, 3162 downloads, 2 subscriptions

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2014-07-22 Version 4.5

New features

  • Input dependent noise and signal variance.

    • Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Expectation Propagation for Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication. Preprint http://arxiv.org/abs/1404.5443
  • Sparse stochastic variational inference model.

    • Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes for big data. arXiv preprint http://arxiv.org/abs/1309.6835.
  • Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the posterior predictive distribution of the latent variable.

    • Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, 57(6):1317-1339.

    • Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution and Asymmetric Principal Components Analysis. Communications in Statistics - Theory and Methods, 35(6):1123-1140.

Improvements

  • New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
  • Precomputed demo results (including the figures) are now available in the folder tests/realValues.
  • New demos demonstrating new features etc.
    • demo_epinf, demonstrating the input dependent noise and signal variance model
    • demo_svi_regression, demo_svi_classification
    • demo_modelcomparison2, demo_survival_comparison

Several minor bugfixes


Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22904 views, 6881 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


Logo pyGPs 1.2

by mn - July 17, 2014, 10:28:55 CET [ Project Homepage BibTeX Download ] 1738 views, 422 downloads, 2 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

Changelog pyGPs v1.2

June 30th 2014

structural updates:

  • input target now can either be in 2-d array with size (n,1) or in 1-d array with size (n,)
  • setup.py updated
  • "import pyGPs" instead of "from pyGPs.Core import gp"
  • rename ".train()" to ".optimize()"
  • rename "Graph-stuff" to "graphExtension"
  • rename kernelOnGraph to "nodeKernels" and graphKernel to "graphKernels"
  • redundancy removed for model.setData(x,y)
  • rewrite "mean.proceed()" to "getMean()" and "getDerMatrix()"
  • rewrite "cov.proceed()" to "getCovMatrix()" and "getDerMatrix()"
  • rename cov.LIN to cov.Linear (to be consistent with mean.Linear)
  • rename module "valid" to "validation"
  • add graph dataset Mutag in python file. (.npz and .mat)
  • add graphUtil.nomalizeKernel()
  • fix number of iteration problem in graphKernels "PropagationKernel"
  • add unit testing for covariance, mean functions

bug fixes:

  • derivatives for cov.LINard
  • derivative of the scalar for cov.covScale
  • demo_GPR_FITC.py missing pyGPs.mean

July 8th 2014

structural updates:

  • add hyperparameter(signal variance s2) for linear covariance
  • add unit testing for inference,likelihood functions as well as models
  • NOT show(print) "maximum number of sweep warning in inference EP" any more
  • documentation updated

bug fixes:

  • typos in lik.Laplace
  • derivative in lik.Laplace

July 14th 2014

documentation updates:

  • online docs updated
  • API file updated

structural updates:

  • made private for methods that users don't need to call

Logo Crino 1.0.0

by jlerouge - July 16, 2014, 17:54:55 CET [ Project Homepage BibTeX Download ] 416 views, 77 downloads, 2 subscriptions

About: Crino: a neural-network library based on Theano

Changes:

1.0.0 (7 july 2014) : - Initial release of crino - Implements a torch-like library to build artificial neural networks (ANN) - Provides standard implementations for : * auto-encoders * multi-layer perceptrons (MLP) * deep neural networks (DNN) * input output deep architecture (IODA) - Provides a batch-gradient backpropagation algorithm, with adaptative learning rate


Logo ABACOC Adaptive Ball Cover for Classification 1.0

by kikot - July 14, 2014, 16:27:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 447 views, 105 downloads, 3 subscriptions

About: Online Action Recognition via Nonparametric Incremental Learning. Java and Matlab code already available. A Python version and the Java source code will be released soon.

Changes:

Initial release of the library, future changes will be advertised shortly.


Logo ARTOS Adaptive Realtime Object Detection System 1.0

by erik - July 11, 2014, 22:02:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 557 views, 88 downloads, 2 subscriptions

About: ARTOS can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories.

Changes:

Initial Announcement on mloss.org.


Logo RankSVM NC 1.0

by rflamary - July 10, 2014, 15:51:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 557 views, 119 downloads, 1 subscription

About: This package is an implementation of a linear RankSVM solver with non-convex regularization.

Changes:

Initial Announcement on mloss.org.


Logo PyStruct 0.2

by t3kcit - July 9, 2014, 09:29:23 CET [ Project Homepage BibTeX Download ] 1484 views, 427 downloads, 1 subscription

About: PyStruct is a framework for learning structured prediction in Python. It has a modular interface, similar to the well-known SVMstruct. Apart from learning algorithms it also contains model formulations for popular CRFs and interfaces to many inference algorithm implementation.

Changes:

Initial Announcement on mloss.org.


Logo Semi Stochastic Gradient Descent 1.0

by konkey - July 9, 2014, 04:28:47 CET [ BibTeX BibTeX for corresponding Paper Download ] 499 views, 111 downloads, 1 subscription

About: Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized).

Changes:

Initial Announcement on mloss.org.


Logo IPCA v0.1

by kiraly - July 7, 2014, 10:25:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 560 views, 102 downloads, 1 subscription

About: This package implements Ideal PCA in MATLAB. Ideal PCA is a (cross-)kernel based feature extraction algorithm which is (a) a faster alternative to kernel PCA and (b) a method to learn data manifold certifying features.

Changes:

Initial Announcement on mloss.org.


Showing Items 21-30 of 537 on page 3 of 54: Previous 1 2 3 4 5 6 7 8 Next Last