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Logo MLweb 0.1.2

by lauerfab - October 9, 2015, 11:55:52 CET [ Project Homepage BibTeX Download ] 783 views, 195 downloads, 2 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

  • Add Regression:AutoReg method
  • Add KernelRidgeRegression tuning function
  • More efficient predictions for KRR, SVM, SVR
  • Add BFGS optimization method
  • Faster QR, SVD and eigendecomposition
  • Better support for sparse vectors and matrices
  • Add linear algebra benchmark at
  • Fix plots in LALOlib/ML.js
  • Fix cross-origin issues in new MLlab()
  • Small bug fixes

Logo NPD Face Detector Training 1.0

by openpr_nlpr - October 8, 2015, 04:22:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 133 views, 36 downloads, 1 subscription

About: This MATLAB package provides the Deep Quadratic Tree (DQT) and the Normalized Pixel Difference (NPD) based face detector training method proposed in our PAMI 2015 paper. It is fast, and effective for unconstrained face detection. For more details, please visit


Initial Announcement on

Logo Armadillo library 6.100

by cu24gjf - October 3, 2015, 07:12:38 CET [ Project Homepage BibTeX Download ] 64890 views, 13187 downloads, 5 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use, with a function syntax similar to MATLAB. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, OpenBLAS).

  • faster norm() and normalise() when using Intel MKL, ATLAS or OpenBLAS
  • faster handling of compound expressions by join_rows() and join_cols()
  • added Schur decomposition: schur()
  • added .each_slice() for repeated matrix operations on each slice of a cube
  • expanded join_slices() to handle joining cubes with matrices
  • expanded .each_col() and .each_row() to handle out-of-place operations
  • stricter handling of matrix objects by hist() and histc()
  • Cube class now delays allocation of .slice() related structures until needed

Logo r-cran-CoxBoost 1.4

by r-cran-robot - October 1, 2015, 00:00:05 CET [ Project Homepage BibTeX Download ] 22339 views, 4491 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks


Fetched by r-cran-robot on 2015-10-01 00:00:05.199222

Logo r-cran-e1071 1.6-7

by r-cran-robot - October 1, 2015, 00:00:05 CET [ Project Homepage BibTeX Download ] 19419 views, 4128 downloads, 2 subscriptions

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About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly


Fetched by r-cran-robot on 2015-10-01 00:00:05.318823

Logo r-cran-Boruta 5.0.0

by r-cran-robot - October 1, 2015, 00:00:04 CET [ Project Homepage BibTeX Download ] 13035 views, 2766 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection


Fetched by r-cran-robot on 2015-10-01 00:00:04.647336

Logo Somoclu 1.5

by peterwittek - September 30, 2015, 13:27:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9058 views, 1770 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, R, and MATLAB are supported.

  • New: Python interface has visual capabilities.
  • New: Option for hexagonal grid.
  • New: Option for requesting compact support in updating the map.
  • New: Python, R, and MATLAB interfaces now allow passing an initial codebook.
  • Changed: Reduced memory use in calculating U-matrices.
  • Changed: Build system rebuilt and simplified.

Logo Optunity 1.1.1

by claesenm - September 30, 2015, 07:06:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3490 views, 885 downloads, 2 subscriptions

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.


This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

Logo DiffSharp 0.7.0

by gbaydin - September 29, 2015, 14:09:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3049 views, 629 downloads, 3 subscriptions

About: DiffSharp is an automatic differentiation (AD) library providing gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products. It allows exact and efficient calculation of derivatives, with support for nesting.


Version 0.7.0 is a reimplementation of the library with support for linear algebra primitives, BLAS/LAPACK, 32- and 64-bit precision and different CPU/GPU backends

Changed: Namespaces have been reorganized and simplified. This is a breaking change. There is now just one AD implementation, under DiffSharp.AD (with DiffSharp.AD.Float32 and DiffSharp.AD.Float64 variants, see below). This internally makes use of forward or reverse AD as needed.

Added: Support for 32 bit (single precision) and 64 bit (double precision) floating point operations. All modules have Float32 and Float64 versions providing the same functionality with the specified precision. 32 bit floating point operations are significantly faster (as much as twice as fast) on many current systems.

Added: DiffSharp now uses the OpenBLAS library by default for linear algebra operations. The AD operations with the types D for scalars, DV for vectors, and DM for matrices use the underlying linear algebra backend for highly optimized native BLAS and LAPACK operations. For non-BLAS operations (such as Hadamard products and matrix transpose), parallel implementations in managed code are used. All operations with the D, DV, and DM types support forward and reverse nested AD up to any level. This also paves the way for GPU backends (CUDA/CuBLAS) which will be introduced in following releases. Please see the documentation and API reference for information about how to use the D, DV, and DM types. (Deprecated: The FsAlg generic linear algebra library and the Vector<'T> and Matrix<'T> types are no longer used.)

Fixed: Reverse mode AD has been reimplemented in a tail-recursive way for better performance and preventing StackOverflow exceptions encountered in previous versions.

Changed: The library now uses F# 4.0 (FSharp.Core

Changed: The library is now 64 bit only, meaning that users should set "x64" as the platform target for all build configurations.

Fixed: Overall bug fixes.

Logo Chalearn gesture challenge code by jun wan 2.0

by joewan - September 29, 2015, 08:50:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 3649 views, 920 downloads, 1 subscription

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.


Initial Announcement on

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