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In this work, we improved this technique by using a learning schema, namely the Exploration-Exploitation using Exponential weight (EXP3), that allows smart resource allocations.
In the learning phase a model that represents the best distribution of the involved codewords in each category of scenes was built by using a learning algorithm based on Latent Dirichlet Allocation [35].
This can be realized by using a learning algorithm to associate the "predictiveness strength" of a network with the assessment of the accuracy of the learning algorithm trained on the network itself.
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We took advantage of this for link prediction by using a learning-to-rank framework that may rank high items even if some features do not rank them high.
A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach.
The solution presents a clear improvement on a tracking algorithm achieved by using a machine learning approach for visual object detection and recognition fordata association.
Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples.
We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging.
In this paper, we explore this new level of image understanding with the ultimate goal of relating a set of image preferences to personality traits by using a deep learning framework.
These findings demonstrate the efficacy of the educational intervention in rapidly bringing the junior residents to an adequate level of proficiency by using a blended learning approach.
This is beneficial for the whole network, since it can determine the most appropriate uplink-to-downlink ratio, e.g., by using a machine learning algorithm.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com