RNN-generated arXiv abstracts


I scraped the entirety of arXiv abstracts to do some experiments. To get started, I trained a char-rnn on all the q-bio abstracts and generated a bunch of synthetic abstracts. Some of the results were quite fun, see below:

Various brain areas reveal spatiotemporal activity patterns that repeat over time: resulting intracellular elements of genetic regulatory networks are quantified. Using a ” experimental study of neural networks, the framework of cellular Markov models to the importance of complexity induces a identification of challenges for understanding specialized biological structures.


Modelling forest composition function for meaningful laws in cortical networks, in the light of simplifying assumption of interaction networks with the same importance they exploit networks used by previous models in topological detail. Existing methods largely depend on a kinetic SIR model under physical networks. We have used the stationary law of overlapping phylogenetic tree distributions as a popular utility. Making use of eigenvalue laws and a scheme augmented along the population and eventually simplify a network .


It also tries to generate LaTeX but it doesn’t get it quite right yet:
Geometry of DNA looping where the residence of 26 ‘ alleles diffusing out than amplitude distributions ( $ F ( x ) $ -test are abrupt at short times $ O ( n = 0.5 ) < $ ^ { 2+ } $ due to a balance matrix , and the synergism of the model and a statistical mechanics level comparable .
I experimented with generating arXiv categories and titles along with the abstracts.
Categories: q-bio.PE stat.AP stat.ME
Title: Joint Resolution Basis GDDA-BLAST Reaction: Mechanism of Biodynamics Waves problems in swarms
Abstract: In a reply that is robust from male molecules in the ecosystem and have presented to apply it to the city in proteomics evolution. An important entity presently processing a MS/MS spectrum outbreak, monitoring, requires on tests but not only an important difficulty in big datasets, opening gained from the usual graph lens and (human) sensitivity analysis. Future dimension test subjects are valid how the epigenetic basis for protein sequence directionality increases the increase within size state. We corrected review particular methods.