To the Hacker News community, as I'm genuinely curious, does this link contribute anything to you or your understanding of deep learning? 29 people voted for it

To the Hacker News community, as I'm genuinely curious, does this link contribute anything to you or your understanding of deep learning? 29 people voted for it, zero comments, and it's still on the front page after five hours. It's a very technical and very specific paper that has not yet had any real analysis by the broader ML community. From the previous discussions on deep learning, I generally suspect that seeing "neural networks" is an upvote trigger but then very little good discussion continues past that. I work in the field and I might read this later - but that's honestly only a might. The datasets they examine aren't particularly impactful or interesting and the paper is preliminary. MNIST is a standard complained about dataset in vision (with someone recently noting it's more a unit test than a benchmark) but is infrequently used as a dataset for RNNs, other than potentially permuted MNIST which isn't used here. The IMDb dataset is at least standard for RNNs but also not representative of the complexity of many sequence tasks. The primary statement being made is that the simpler LSTM1/2/3 can achieve similar numbers to that of the LSTM when using a proper hyper-parameter search. That's potentially useful to know but also likely not the thing stopping practitioners from putting such work into the field. Many architectures are also limited in the number of times they can be trained due to computational restrictions - otherwise I'd usually strongly suggest hyper-parameter search! If people are interested in this type of analysis over RNN architectures, I recommend the older but still useful "An Empirical Exploration of Recurrent Network Architectures"[1]. The primary contribution there is that forget gates should be set to 1 for LSTMs, which was used and then forgotten for many years, but they do present various LSTM variants (MUT1/2/3) that are more computationally efficient too. These were integrated into Keras (a Python machine learning library) for some time. They also show their results over more datasets (arithmetic, XML modeling, language modeling on PTB, and music prediction) for a more convincing and nuanced discussion. P.S. I'll note I saw this on Nando de Freitas' Twitter feed and assume that's why it was posted here (given he's a Professor of Computer Science at the University of Oxford and a senior researcher at Google). A retweet doesn't constitute an endorsement though, especially in science. I'm still confused as to why Hacker News, a very general crowd in tech, care particularly for one deep learning paper and not another. Color me confused :) [1]: https://research.google.com/pubs/pub45473.html https://research.google.com/pubs/pub45473.html