• SHOGUN

  • Referenced in 103 articles [sw03517]
  • SHOGUN, which is designed for unified large-scale learning for a broad range of feature...
  • PETSc

  • Referenced in 1573 articles [sw04012]
  • building blocks for the implementation of large-scale application codes on parallel (and serial) computers ... users it initially has a much steeper learning curve than a simple subroutine library...
  • SVMTorch

  • Referenced in 67 articles [sw12121]
  • Joachims [“Making large-scale support vector machine learning practical”, in: B. Schölkopf, C. Burges ... algorithm, one can now efficiently solve large-scale regression problems (more than 20000 examples). Comparisons ... large-scale regression problems from G. Flake and S. Lawrence [Mach. Learn...
  • SVMlight

  • Referenced in 264 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...
  • LIBOL

  • Referenced in 9 articles [sw10887]
  • open-source library for large-scale online learning, which consists of a large family ... state-of-the-art online learning algorithms for large-scale online classification tasks. We have ... users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform...
  • GeneNet

  • Referenced in 8 articles [sw07992]
  • Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment...
  • LAMG

  • Referenced in 34 articles [sw06551]
  • graphs arise in large-scale computational applications such as semisupervised machine learning; spectral clustering...
  • SimpleMKL

  • Referenced in 68 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 ... that encourages sparse kernel combinations. Apart from learning the combination, we solve a standard...
  • MLlib

  • Referenced in 25 articles [sw15430]
  • large-scale data processing that is well-suited for iterative machine learning tasks. In this...
  • Cityscapes

  • Referenced in 19 articles [sw36624]
  • enormously from large-scale datasets, especially in the context of deep learning. For semantic urban ... introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches...
  • DistilBERT

  • Referenced in 6 articles [sw30758]
  • cheaper and lighter. As Transfer Learning from large-scale pre-trained models becomes more prevalent ... Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained ... faster. To leverage the inductive biases learned by larger models during pre-training, we introduce...
  • SOFAR

  • Referenced in 7 articles [sw31665]
  • SOFAR: large-scale association network learning. Many modern big data applications feature large scale ... enabled by understanding the large-scale response-predictor association network structures via layers of sparse ... sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this ... value decomposition with orthogonality constrained optimization to learn the underlying association networks, with broad applications...
  • GURLS

  • Referenced in 3 articles [sw10895]
  • supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists ... training strategies for medium and large-scale learning, and routines for efficient model selection...
  • DGL-KE

  • Referenced in 3 articles [sw34088]
  • Training Knowledge Graph Embeddings at Scale. Knowledge graphs (KGs) are data structures that store information ... approach of using KGs in various machine learning tasks is to compute knowledge graph embeddings ... scalable package for learning large-scale knowledge graph embeddings. The package is implemented...
  • SLEP

  • Referenced in 41 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...
  • OpenKE

  • Referenced in 5 articles [sw30611]
  • support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity ... toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE...
  • DeepLOB

  • Referenced in 4 articles [sw40384]
  • order books. We develop a large-scale deep learning model to predict price movements from...
  • sparsebn

  • Referenced in 2 articles [sw21046]
  • Learning Large-Scale Bayesian Networks with the sparsebn Package. Learning graphical models from data...
  • LIBS2ML

  • Referenced in 1 article [sw28299]
  • scalable second order learning algorithms for solving large-scale problems, i.e., big data problems ... advantage of both the worlds, i.e., faster learning using C++ and easy I/O using MATLAB ... very slow and not suitable for large-scale learning, or are in C/C++ which does ... effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source...
  • Spark

  • Referenced in 41 articles [sw23653]
  • have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However ... parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis...