| Actually, yes, the QRNN has all of those features. First figure from our paper: how the LSTM with a twist allows for the equivalent speed of a plain convnet by running efficiently in parallel on GPUs, like image processing convents.[1] Best of all, as it's only an "LSTM with (these) twists", it's drop-in compatible with existing LSTMs but can get you a 2-17 times speed-up over NVIDIA's cuDNN LSTM - essentially speed equivalent to the TCN or WaveNet speed-up. That's why Baidu implemented QRNN in their production Deep Voice 2 neural text-to-speech (TTS) system[3]. This isn't to say TCN or QRNN is better, simply that it's dangerous to flat out say _no_ if you're not actually certain or don't correctly recall the underlying information. Disclaimer: I'm the co-author of the QRNN. Double disclaimer: The TCN paper cites the QRNN but decides not to test against it. They also show results over one of my datasets. [1]: https://www.semanticscholar.org/paper/Quasi-Recurrent-Neural... [2]: https://github.com/salesforce/pytorch-qrnn [3]: https://www.semanticscholar.org/paper/Deep-Voice-2%3A-Multi-... |