Search results
Results from the WOW.Com Content Network
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 number of solutions to the problem have appeared in literature, notably Davenport's q-method, [2] QUEST and methods based on the singular value decomposition (SVD). Several methods for solving Wahba's problem are discussed by Markley and Mortari.
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 ...
Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation. Distributed problem solving (DPS): the concept of agent , autonomous entities that can communicate with each other, was developed to serve as an ...
Other than language models, Vision MoE [36] is a Transformer model with MoE layers. They demonstrated it by training a model with 15 billion parameters. MoE Transformer has also been applied for diffusion models. [37] A series of large language models from Google used MoE. GShard [38] uses MoE with up to top-2 experts per layer. Specifically ...
The ensemble Kalman filter (EnKF) is a Monte Carlo implementation of the Bayesian update problem: given a probability density function (PDF) of the state of the modeled system (the prior, called often the forecast in geosciences) and the data likelihood, Bayes' theorem is used to obtain the PDF after the data likelihood has been taken into account (the posterior, often called the analysis).
In the worst-case, the first presented example is entirely new, and gives bits of information, but each subsequent example would differ minimally from previous examples, and gives 1 bit each. After n + 1 {\displaystyle n+1} examples, there are 2 n {\displaystyle 2n} bits of information, which is sufficient for the perceptron (with 2 n ...
For example, LOBPCG implementations, [9] [10] follow, [8] [11] separating hard locking, i.e. a deflation by restriction, where the locked eigenvectors serve as a code input and do not change, from soft locking, where the locked vectors do not participate in the typically most expensive iterative step of computing the residuals, however, fully ...