SciRate gives instant gratification for our precious preprints on arXiv. We analyse the metadata of the papers that appeared in quant-ph in 2016 to find out the hottest authors and topics, and we train a recurrent neural network to generate fake abstracts.
Posts tagged: Machine learning
Quantum machine learning as a research field is exploding: here we give a brief overview of the relevant papers that appeared on arXiv in 2015.
Somoclu, a massively parallel implementation of self-organizing maps, has updated its visual capabilities for its Python interface.
Looking at the crop of quantum machine learning manuscripts on arXiv from the beginning of 2015 until the middle of May.
Reflective random indexing can lead to strange results: if the vocabulary is small compared to the number of documents, term vectors will show little variety.
Another handful of papers on quantum machine learning that appeared in the last two months of 2014, and perhaps slightly earlier.
A quick overview of a handful of papers on quantum machine learning that appeared recently.
New characterizations of Bell inequalities in terms of causal structures are emerging: they can give rise to quantum versions of Bayesian networks.
Classical regression, induction, transduction and the quantum learning of unitaries, plus making the difference explicit to process tomography.
Self-organizing maps are computationally expensive to train -- emergent maps are even more so. This post looks at the constraints with sparse data.
The optimal estimation of a group of unitary transforms allows for learning an unknown function: this is similar to regression in classical machine learning.
Training least squares support vector machines on quantum hardware results in exponential speedup; we take a machine learning perspective at the new algorithm.
Describing how to build a complex-valued random index using a term and a concept vector space.