will wolf

#artificialintelligence

Bayesian probabilistic models provide a nimble and expressive framework for modeling "small-world" data. In contrast, deep learning offers a more rigid yet much more powerful framework for modeling data of massive size. Edward is a probabilistic programming library that bridges this gap: "black-box" variational inference enables us to fit extremely flexible Bayesian models to large-scale data. Furthermore, these models themselves may take advantage of classic deep-learning architectures of arbitrary complexity. Edward uses TensorFlow for symbolic gradients and data flow graphs.


Overview of Udacity Artificial Intelligence Engineer Nanodegree, Term 1

#artificialintelligence

After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs.


Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions

arXiv.org Machine Learning

Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed. However, in many financial applications, the assumption is quite limiting in two aspects. First, even when the data follows a Gaussian distribution, its variance may change over time. Such variance is also related to important temporal economic indicators such as the market volatility. Second, financial time-series data often requires learning datasets generated by the generalized Gaussian distribution with an additional shape parameter that is important to approximate heavy-tailed distributions. Addressing those aspects, we show how to extend DyBM that results in significant performance improvement in predicting financial time-series data.


Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines

arXiv.org Machine Learning

This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm belongs to the family of truncated Newton methods and exploits an efficient matrix-vector product to avoid explicitely storing the natural gradient metric $L$. This metric is shown to be the expected second derivative of the log-partition function (under the model distribution), or equivalently, the variance of the vector of partial derivatives of the energy function. We evaluate our method on the task of joint-training a 3-layer Deep Boltzmann Machine and show that MFNG does indeed have faster per-epoch convergence compared to Stochastic Maximum Likelihood with centering, though wall-clock performance is currently not competitive.