ArXiving Before Submission Helps Everyone

We claim, and present evidence, that allowing arXiv publication before a conference or journal submission benefits researchers, especially early career, as well as the whole scientific community. Specifically, arXiving helps professional identity building, protects against independent re-discovery, idea theft and gate-keeping; it facilitates open research result distribution and reduces inequality. The advantages dwarf the drawbacks -- mainly the relative increase in acceptance rate of papers of well-known authors -- which studies show to be marginal. Analyzing the pros and cons of arXiving papers, we conclude that requiring preprints be anonymous is nearly as detrimental as not allowing them. We see no reasons why anyone but the authors should decide whether to arXiv or not.

[1]  Beronda L. Montgomery,et al.  Building and Sustaining Diverse Functioning Networks Using Social Media and Digital Platforms to Improve Diversity and Inclusivity , 2018, Front. Digit. Humanit..

[2]  Nicholay Topin,et al.  Super-convergence: very fast training of neural networks using large learning rates , 2018, Defense + Commercial Sensing.

[3]  Nihar B. Shah,et al.  On Testing for Biases in Peer Review , 2019, NeurIPS.

[4]  Waleed Ammar,et al.  Citation Count Analysis for Papers with Preprints , 2018, ArXiv.

[5]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[6]  Quoc V. Le,et al.  Searching for Activation Functions , 2018, arXiv.

[7]  Min Zhang,et al.  Reviewer bias in single- versus double-blind peer review , 2017, Proceedings of the National Academy of Sciences.

[8]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[9]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[10]  Scott Krig,et al.  Computer Vision Metrics , 2014, Apress.

[11]  Leslie N. Smith,et al.  Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Richard R. Hamming.  The Art of Doing Science and Engineering: Learning to Learn , 1997 .

[14]  Leslie N. Smith,et al.  A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay , 2018, ArXiv.

[15]  Kevin Gimpel,et al.  Gaussian Error Linear Units (GELUs) , 2016 .

[16]  Animesh Garg,et al.  De-anonymization of authors through arXiv submissions during double-blind review , 2020, ArXiv.