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If you are reeling from a NeurIPS rejection or stressing about an ICLR submission, remember that some of the best papers were never published anywhere except arxiv. Thread of a few favorites (1/5):
"Generating Sequences with RNNs" by Graves https://arxiv.org/abs/1308.0850 This paper blew my mind when it came out, showing that it was possible to generate plausible text and handwriting with RNNs. Includes the predecessors of attention, Adam, etc... (2/5)
WaveNet by van den Oord et al. https://arxiv.org/abs/1609.03499 Until this came out I don't think most of us expected that we'd be able to generate raw waveforms with deep networks anytime soon. The results were surprisingly good and the architecture remains influential. (3/5)
"Learning to Generate Reviews and Discovering Sentiment" by Radford et al. https://arxiv.org/abs/1704.01444 A simple and surprising result (thresholding a neuron in an unsupervised LM could classify sentiment accurately) that helped kicked off the transfer learning craze in NLP. (4/5)
"Implicit Autoencoders" by Makhzani https://arxiv.org/abs/1805.09804 A deeper cut, this paper (loosely speaking) proposes a VAE where both the "reconstruction" and "regularization" terms are replaced with adversarial losses. Impressive results on disentangling style and content. (5/5)
Don't you think the papers you listed were from the authors who were already famous, and people followed their work, but for a new student at not so famous lab this is not an option, no one is going to read their arxiv preprint
Mostly agree; arxiv-only is a privilege often exercised only by those who are not under pressure to publish at conferences. I was making a different point - just that there have been great arxiv-only papers, so if your paper gets rejected, it doesn't mean it won't be influential!
Layer Normalization, by Jimmy Ba, Jamie Kiros, and Geoffrey Hinton (https://arxiv.org/abs/1607.06450 ). Crucial for Transformer to work, amount many others!
Good one! I thought this was at NeurIPS for some reason.
DCGAN. I was really surprised when I was searching for its bibtex entry as a proceedings publication.
DCGAN was actually at ICLR 2016! https://iclr.cc/archive/www/doku.php%3Fid=iclr2016:main.html …
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