
SHOGUN
 Referenced in 103 articles
[sw03517]
 SHOGUN, which is designed for unified largescale learning for a broad range of feature...

PETSc
 Referenced in 1573 articles
[sw04012]
 building blocks for the implementation of largescale 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 largescale support vector machine learning practical”, in: B. Schölkopf, C. Burges ... algorithm, one can now efficiently solve largescale regression problems (more than 20000 examples). Comparisons ... largescale 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 largescale transductive SVMs. The algorithm proceeds by solving...

LIBOL
 Referenced in 9 articles
[sw10887]
 opensource library for largescale online learning, which consists of a large family ... stateoftheart online learning algorithms for largescale 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 largescale gene association networks (including assignment...

LAMG
 Referenced in 34 articles
[sw06551]
 graphs arise in largescale 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 semiinfinite linear programming ... since it makes MKL tractable for largescale 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]
 largescale data processing that is wellsuited for iterative machine learning tasks. In this...

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

DistilBERT
 Referenced in 6 articles
[sw30758]
 cheaper and lighter. As Transfer Learning from largescale pretrained models becomes more prevalent ... Natural Language Processing (NLP), operating these large models in ontheedge and/or under constrained ... faster. To leverage the inductive biases learned by larger models during pretraining, we introduce...

SOFAR
 Referenced in 7 articles
[sw31665]
 SOFAR: largescale association network learning. Many modern big data applications feature large scale ... enabled by understanding the largescale responsepredictor 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 nonspecialists ... training strategies for medium and largescale learning, and routines for efficient model selection...

DGLKE
 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 largescale knowledge graph embeddings. The package is implemented...

SLEP
 Referenced in 41 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...

OpenKE
 Referenced in 5 articles
[sw30611]
 support quick model validation and largescale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity ... toolkit, the embeddings of some existing largescale knowledge graphs pretrained by OpenKE...

DeepLOB
 Referenced in 4 articles
[sw40384]
 order books. We develop a largescale deep learning model to predict price movements from...

sparsebn
 Referenced in 2 articles
[sw21046]
 Learning LargeScale Bayesian Networks with the sparsebn Package. Learning graphical models from data...

LIBS2ML
 Referenced in 1 article
[sw28299]
 scalable second order learning algorithms for solving largescale 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 largescale learning, or are in C/C++ which does ... effective tool to deal with the largescale learning problems. LIBS2ML is an opensource...

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