
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 gradientbased 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...

RMAX
 Referenced in 32 articles
[sw02539]
 very simple modelbased reinforcement learning algorithm which can attain nearoptimal average reward ... algorithm, covering zerosum stochastic games. (2) It has a builtin 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...

CMAES
 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...

SGDQN
 Referenced in 23 articles
[sw19411]
 nearly as fast as a firstorder 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 deeplearning 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 rulebased models written ... KaSim reference manual (available here) to learn about stochastic simulation and KappaLanguage...

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 online learning. In this paper the classic ... momentum algorithm for stochastic optimization is considered. A method is introduced that adjusts coefficients ... method is applied to online learning in feedforward 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...