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Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4000 views, 679 downloads, 2 subscriptions

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

Changes:

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999


Logo pSpectralClustering 1.1

by tbuehler - July 30, 2014, 19:44:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5209 views, 1167 downloads, 2 subscriptions

About: A generalized version of spectral clustering using the graph p-Laplacian.

Changes:
  • fixed compatibility issue with Matlab R2013a+
  • several internal optimizations

Logo QSMM 1.16

by olegvol - July 29, 2014, 19:37:31 CET [ Project Homepage BibTeX Download ] 545 views, 128 downloads, 3 subscriptions

About: The implementation of adaptive probabilistic mappings.

Changes:

Initial Announcement on mloss.org.


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 2879 views, 869 downloads, 3 subscriptions

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

Changes:
  • added ElasticNets as a regularization option
  • fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
  • added a couple of I/O optimizations

Logo JMLR JKernelMachines 2.4

by dpicard - July 24, 2014, 13:51:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12588 views, 3159 downloads, 2 subscriptions

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About: machine learning library in java for easy development of new kernels

Changes:

Version 2.4

  • Added a simple GUI to rapidly test some algorithms
  • New Active Learning package
  • New algorithms (LLSVM, KMeans)
  • New Kernels (Polynomials, component wise)
  • Many bugfixes and improvements to existing algorithms
  • Many optimization

The number of changes in this version is massive, test it! Don't forget to report any regression.


Logo Optunity 0.2.0

by claesenm - July 24, 2014, 10:07:54 CET [ Project Homepage BibTeX Download ] 527 views, 172 downloads, 1 subscription

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.

Changes:

Initial Announcement on mloss.org.


Logo JMLR GPstuff 4.5

by avehtari - July 22, 2014, 14:03:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14501 views, 3576 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 ] 24367 views, 7133 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 Crino 1.0.0

by jlerouge - July 16, 2014, 17:54:55 CET [ Project Homepage BibTeX Download ] 652 views, 146 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 ] 726 views, 184 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.


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