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About: Bayesian statespace modelling and inference on highperformance computer hardware. Changes:Initial Announcement on mloss.org.

About: Nonnegative Sparse Coding, Discriminative Semisupervised Learning, sparse probability graph Changes:Initial Announcement on mloss.org.

About: In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEntPCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2norm or L1norm, the MaxEntPCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEntPCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEntPCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on realworld datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods. Changes:Initial Announcement on mloss.org.

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.

About: Efficient implementation of SemiStochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2regularized). Changes:Initial Announcement on mloss.org.

About: CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow(https://github.com/tqchen/mshadow). It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance. Changes:Initial Announcement on mloss.org.

About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 1839, January 2008. Changes:Initial Announcement on mloss.org.

About: DDN learns and visualize differential dependency networks from conditionspecific data. Changes:Initial Announcement on mloss.org.
