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I've been using a QRNN in PyTorch, is this similar to a TCN? https://github.com/salesforce/pytorch-qrnn https://github.com/salesforce/pytorch-qrnn

visarga 8y ago
No, TCN is similar to WaveNet (dilated convolutions + masking the future + residual connections). It's a plain convnet, not an LSTM with a twist. That's why it runs efficiently in parallel on GPUs, like image processing convnets.
- Smerity 8y ago
  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-Networks-Bradbury-Merity/2d876ed1dd2c58058d7197b734a8e4d349b8f231/figure/0 https://www.semanticscholar.org/paper/Quasi-Recurrent-Neural... [2]: https://github.com/salesforce/pytorch-qrnn https://github.com/salesforce/pytorch-qrnn [3]: https://www.semanticscholar.org/paper/Deep-Voice-2%3A-Multi-Speaker-Neural-Text-to-Speech-Gibiansky-Arik/522a7178e501018e442c03f4b93e29f62ae1eb64 https://www.semanticscholar.org/paper/Deep-Voice-2%3A-Multi-...
  - visarga 8y ago
    Haha, small world. I knew about QRNN and even tried to find an implementation once to test it on my data. Neat idea.

visarga 8y ago

No, TCN is similar to WaveNet (dilated convolutions + masking the future + residual connections). It's a plain convnet, not an LSTM with a twist. That's why it runs efficiently in parallel on GPUs, like image processing convnets.

Smerity 8y ago
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-Networks-Bradbury-Merity/2d876ed1dd2c58058d7197b734a8e4d349b8f231/figure/0 https://www.semanticscholar.org/paper/Quasi-Recurrent-Neural... [2]: https://github.com/salesforce/pytorch-qrnn https://github.com/salesforce/pytorch-qrnn [3]: https://www.semanticscholar.org/paper/Deep-Voice-2%3A-Multi-Speaker-Neural-Text-to-Speech-Gibiansky-Arik/522a7178e501018e442c03f4b93e29f62ae1eb64 https://www.semanticscholar.org/paper/Deep-Voice-2%3A-Multi-...
- visarga 8y ago
  Haha, small world. I knew about QRNN and even tried to find an implementation once to test it on my data. Neat idea.