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A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior [3] A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow
A NestedSampler is part of the Python toolbox BayesicFitting [9] for generic model fitting and evidence calculation. It is available on GitHub. An implementation in C++, named DIAMONDS, is on GitHub. A highly modular Python parallel example for statistical physics and condensed matter physics uses is on GitHub.
This problem can be seen as a generalization of the linear assignment problem. [2] In words, the problem can be described as follows: An instance of the problem has a number of agents (i.e., cardinality parameter) and a number of job characteristics (i.e., dimensionality parameter) such as task, machine, time interval, etc. For example, an ...
Torch development moved in 2017 to PyTorch, a port of the library to Python. [4] [5] [6] ... What follows is an example use-case for building a multilayer perceptron ...
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.
It is equivalent to a model-free brute force search in the state space. In contrast, a high-efficiency algorithm has a low sample complexity. [11] Possible techniques for reducing the sample complexity are metric learning [12] and model-based reinforcement learning. [13]
This relationship may be derived by noting that the absolute value of the Jacobian determinant of this change of variables is |C| p+1, see for example equation (15.15) in. [25] In Bayesian statistics, the Wishart distribution is a conjugate prior for the precision parameter of the multivariate normal distribution, when the mean parameter is ...