• 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 large-scale 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) First-Order Method. At each iteration ... thus the algorithms can handle large-scale 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 semi-infinite linear programming ... since it makes MKL tractable for large-scale 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 large-scale 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 large-scale response-predictor 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]
  • large-scale linear classification, regression and ranking in Python. Highlights: follows the scikit-learn ... 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 large-scale real applications from human and mammary gland gene...
  • NetSMF

  • Referenced in 1 article [sw37752]
  • Embedding as Sparse Matrix Factorization. We study the problem of large-scale network embedding, which ... present the algorithm of large-scale 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 large-scale academic collaboration network with tens...
  • DiSMEC

  • Referenced in 4 articles [sw30154]
  • DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification. Extreme multi-label classification refers ... supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets ... DiSMEC, which is a large-scale distributed framework for learning one-versus-rest 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 spike-and-slab sparse coding approach ... machines for medium to large-scale applications. Typical large-scale runs of the algorithms...
  • sparsebn

  • Referenced in 2 articles [sw21046]
  • Learning Large-Scale 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. Multi-task 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 large-scale multi-response regression with incomplete outcomes, where ... predictors can be high-dimensional. Motivated by sparse factor regression, we convert the multi-response...
  • GoGP

  • Referenced in 1 article [sw23912]
  • handle large-scale 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 large-scale datasets, but also...
  • scalpel

  • Referenced in 2 articles [sw19378]
  • publicly-available. The availability of this large-scale 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 large-scale 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 large-scale networks is computationally challenging for most existing ... embeddings efficiently by formulating the task as sparse matrix factorization. The second step of ProNE...
  • DSelect-k

  • Referenced in 1 article [sw41672]
  • parameter sharing in multi-task learning (MTL) and in scaling high-capacity 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 Top-k, are not smooth ... gates. Notably, on a real-world, large-scale recommender system, DSelect-k achieves over...
  • ADOL-C

  • Referenced in 257 articles [sw00019]
  • ADOL-C: 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...