• AdaGrad

  • Referenced in 89 articles [sw22202]
  • algorithm; Adaptive subgradient methods for online learning and stochastic optimization. We present a new family ... iterations to perform more informative gradient-based learning. Metaphorically, the adaptation allows us to find ... stems from recent advances in stochastic optimization and online learning which employ proximal functions...
  • Duali

  • Referenced in 26 articles [sw01245]
  • work with deterministic and passive learning stochastic models. In contrast, Dualpc is primarily a research ... well as both passive and active learning stochastic control models. Models developed in Duali ... deterministic and then as passive learning stochastic models can be exported in the proper format ... solution as either passive or active learning stochastic control models in the Dualpc software...
  • R-MAX

  • Referenced in 32 articles [sw02539]
  • very simple model-based reinforcement learning algorithm which can attain near-optimal average reward ... algorithm, covering zero-sum stochastic games. (2) It has a built-in mechanism for resolving ... Tennenholtz’s LSG algorithm for learning in single controller stochastic games. (5) It generalizes...
  • DualPC

  • Referenced in 10 articles [sw08479]
  • interface for deterministic, passive and active learning stochastic models as well as solvers for deterministic ... models and for passive learning stochastic models. It does not yet contain a solver...
  • Pegasos

  • Referenced in 93 articles [sw08752]
  • training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require ... resulting algorithm is especially suited for learning from large datasets. Our approach also extends...
  • CMA-ES

  • Referenced in 100 articles [sw05063]
  • generated by variation, usually in a stochastic way, and then some individuals are selected ... Adaptation of the covariance matrix amounts to learning a second order model of the underlying...
  • SGD-QN

  • Referenced in 23 articles [sw19411]
  • nearly as fast as a first-order stochastic gradient descent but requires less iterations ... track” of the first PASCAL large scale learning challenge...
  • HOGWILD

  • Referenced in 42 articles [sw28396]
  • Free Approach to Parallelizing Stochastic Gradient Descent. Stochastic Gradient Descent (SGD) is a popular algorithm ... performance on a variety of machine learning tasks. Several researchers have recently proposed schemes...
  • SMART_

  • Referenced in 33 articles [sw04097]
  • simulation is always applicable regardless of the stochastic nature of the process, but certain classes ... classroom and realistic industrial settings as a learning, research, and application tool, it is written...
  • SeqGAN

  • Referenced in 7 articles [sw26534]
  • data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation...
  • CNTK

  • Referenced in 9 articles [sw21056]
  • Toolkit (https://cntk.ai), is a unified deep-learning toolkit that describes neural networks ... networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation...
  • Dynare

  • Referenced in 69 articles [sw12305]
  • class of economic models, in particular dynamic stochastic general equilibrium (DSGE) and overlapping generations ... hence, form their expectations through a learning process. In terms of types of agents, models...
  • Nieme

  • Referenced in 1 article [sw14441]
  • LeCun et al., 2006) which unifies several learning algorithms ranging from simple perceptrons to recent ... This framework also unifies batch and stochastic learning which are both seen as energy minimization...
  • SINE

  • Referenced in 1 article [sw32344]
  • effects of missing information on representation learning. A stochastic gradient descent based online algorithm...
  • ORL

  • Referenced in 1 article [sw34775]
  • Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems. Reinforcement Learning (RL) has achieved state ... algorithms to a selection of canonical online stochastic optimization problems with a range of practical...
  • SMS

  • Referenced in 2 articles [sw26575]
  • simulation and gaming of stochastic market processes and learning behavior...
  • PISKaS

  • Referenced in 1 article [sw29283]
  • Spatial Kappa Simulator. PISKaS is a stochastic simulator for rule-based models written ... KaSim reference manual (available here) to learn about stochastic simulation and Kappa-Language...
  • Anglican

  • Referenced in 2 articles [sw31144]
  • stochastic environment. It is not an academic exercise, but a practical everyday machine learning tool ... unpredictably. Mathematically speaking you observe undeterministic or stochastic behaviour. Anglican allows you to express random ... capturing all this stochasticity for you and helps you to learn from data to execute...
  • ASD+M

  • Referenced in 1 article [sw32914]
  • automatic parameter tuning in stochastic optimization and on-line learning. In this paper the classic ... momentum algorithm for stochastic optimization is considered. A method is introduced that adjusts coefficients ... method is applied to on-line learning in feed-forward neural networks, including deep auto...
  • ProPPR

  • Referenced in 1 article [sw32915]
  • inference. A key challenge in statistical relational learning is to develop a semantically rich formalism ... further extends stochastic logic programs (SLP) to a framework that enables efficient learning and inference ... parameter learning, weight learning can be performed using parallel stochastic gradient descent with a supervised...