Questions tagged [interpolation]

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Continuous state and continuous action Markov decision process time complexity estimate: backward induction VS policy gradient method (RL)

Model Description: Model based(assume known of the entire model) Markov decision process. Time($t$): Finite horizon discrete time with discounting factor State($x_t$): Continuous multi-dimensional ...
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0answers
11 views

How does the bigram terms are contributing to sophisticated version of linear interpolation?

While studying about linear interpolation technique in natural language processing to deal with less frequent $n-$gram. I came across a sophisticated version of linear interpolation. The simple and ...
6
votes
1answer
68 views

Reward interpolation between MDPs. Will an optimal policy on both ends stay optimal inside the interval?

Say I've got two Markov Decision Processes (MDPs): $$\mathcal{M_0} = (\mathcal{S}, \mathcal{A}, P, R_0),\quad\text{and}\quad\mathcal{M}_1 = (\mathcal{S}, \mathcal{A}, P, R_1)$$ Both have the same set ...
0
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0answers
16 views

NN for multivariate function interpolation

I have a multivariate function. It takes 4, real valued inputs: a, b, c, d and returns 1 complex number, z. I wanted to use Neural Networks to predict the value z' for a generic input a', b', c', d'. ...
2
votes
0answers
172 views

What's the nearest neighbor algorithm used for upsampling?

Additionally, by default, the UpSampling2D layer will use a nearest neighbor algorithm to fill in the new rows and columns. This has the effect of simply doubling rows and columns, as described and is ...
1
vote
1answer
35 views

Interpolating image to increase resolution before feeding it to a neural network

Interpolation is a common way to make an image fit the right input shape for a neural network. But is there any point in using interpolation to make it easier for the network to learn? I assume ...