
SVMlight
 Referenced in 268 articles
[sw04076]
 functions [Joachims, 2002c]. The goal is to learn a function from preference examples, so that ... version includes an algorithm for training largescale transductive SVMs. The algorithm proceeds by solving ... code has been used on a large range of problems, including text classification [Joachims, 1999c ... applications. Many tasks have the property of sparse instance vectors. This implementation makes...

SLEP
 Referenced in 42 articles
[sw13487]
 SLEP: Sparse Learning with Efficient Projections. Main Features: 1) FirstOrder Method. At each iteration ... thus the algorithms can handle largescale sparse data. 2) Optimal Convergence Rate. The convergence...

SimpleMKL
 Referenced in 69 articles
[sw12290]
 machine, an efficient and general multiple kernel learning algorithm, based on semiinfinite linear programming ... since it makes MKL tractable for largescale problems, by iteratively using existing support vector ... weights that encourages sparse kernel combinations. Apart from learning the combination, we solve a standard...

FASTCLIME
 Referenced in 14 articles
[sw10889]
 scalable and sophisticated tool for solving largescale linear programs. As an illustrative example ... solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained ... useful to statisticians and machine learning researchers for solving a wide range of problems...

SOFAR
 Referenced in 7 articles
[sw31665]
 scale association network learning. Many modern big data applications feature large scale in both numbers ... understanding the largescale responsepredictor association network structures via layers of sparse latent factors ... sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this ... method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition with orthogonality...

lightning
 Referenced in 3 articles
[sw26590]
 largescale linear classification, regression and ranking in Python. Highlights: follows the scikitlearn ... conventions; supports natively both dense and sparse data representations; omputationally demanding parts implemented in Cython...

BDgraph
 Referenced in 19 articles
[sw14815]
 structure learning in sparse Gaussian graphical models. Decoding complex relationships among large numbers of variables ... practice, sensible approach for structure learning. We illustrate the efficiency of the method ... then apply the method on largescale real applications from human and mammary gland gene...

NetSMF
 Referenced in 1 article
[sw37752]
 Embedding as Sparse Matrix Factorization. We study the problem of largescale network embedding, which ... present the algorithm of largescale network embedding as sparse matrix factorization (NetSMF). NetSMF leverages ... learned embeddings. We conduct experiments on networks of various scales and types. Results show that ... hours to generate effective embeddings for a largescale academic collaboration network with tens...

DiSMEC
 Referenced in 4 articles
[sw30154]
 DiSMEC  Distributed Sparse Machines for Extreme Multilabel Classification. Extreme multilabel classification refers ... supervised multilabel learning involving hundreds of thousands or even millions of labels. Datasets ... DiSMEC, which is a largescale distributed framework for learning oneversusrest linear classifiers ... which is a leading approach for learning sparse local embeddings, and FastXML which...

ProSper
 Referenced in 1 article
[sw30605]
 learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse ... currently includes: Binary Sparse Coding (BSC), Ternary Sparse Coding (TSC), Discrete Sparse Coding (DSC), Maximal ... MMCA), and Gaussian Sparse Coding (GSC, a recent spikeandslab sparse coding approach ... machines for medium to largescale applications. Typical largescale runs of the algorithms...

sparsebn
 Referenced in 2 articles
[sw21046]
 Learning LargeScale Bayesian Networks with the sparsebn Package. Learning graphical models from data ... package called sparsebn for learning the structure of large, sparse graphical models with a focus...

PEER
 Referenced in 1 article
[sw40511]
 response regression with incomplete outcomes. Multitask learning is increasingly used to investigate the association ... data, the coexistence of incomplete outcomes, large number of responses, and high dimensionality in predictors ... computationally efficient procedure, called PEER, for largescale multiresponse regression with incomplete outcomes, where ... predictors can be highdimensional. Motivated by sparse factor regression, we convert the multiresponse...

GoGP
 Referenced in 1 article
[sw23912]
 handle largescale datasets and to accommodate an online learning setting where data arrive irregularly ... novel online Gaussian process model that could scale with massive datasets. Our approach is formulated ... rate our proposed algorithm always produces a (sparse) solution which is close to the true ... method is proven to scale seamlessly not only with largescale datasets, but also...

scalpel
 Referenced in 2 articles
[sw19378]
 publiclyavailable. The availability of this largescale data resource opens the door ... calcium imaging video. We propose a dictionary learning approach for this task. First, we perform ... their corresponding activity over time, using a sparse group lasso optimization problem. We apply...

MFclass
 Referenced in 2 articles
[sw34570]
 prediction of largescale computational models. Machine learning techniques typically rely on large datasets ... thus hindering the potential of machine learning tools. In this work, we present a novel ... classifier to implement active learning strategies. We also introduce a sparse approximation to enhance...

ProNE
 Referenced in 1 article
[sw37757]
 ProNE: Fast and Scalable Network Representation Learning. Recent advances in network embedding has revolutionized ... However, (pre)training embeddings for very largescale networks is computationally challenging for most existing ... embeddings efficiently by formulating the task as sparse matrix factorization. The second step of ProNE...

DSelectk
 Referenced in 1 article
[sw41672]
 parameter sharing in multitask learning (MTL) and in scaling highcapacity neural networks. State ... models use a trainable sparse gate to select a subset of the experts for each ... input example. While conceptually appealing, existing sparse gates, such as Topk, are not smooth ... gates. Notably, on a realworld, largescale recommender system, DSelectk achieves over...

ADOLC
 Referenced in 257 articles
[sw00019]
 ADOLC: Automatic Differentiation of C/C++. We present...

ANSYS
 Referenced in 713 articles
[sw00044]
 ANSYS offers a comprehensive software suite that spans...

ARMS
 Referenced in 68 articles
[sw00048]
 ARMS: an algebraic recursive multilevel solver for general...