An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling · HackerPapers discussions

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling — mentioned by biomodel

Thu, 27 Dec 2018 16:49:41 GMT

Not sure why anyone would use 2D CNNs for processing text when there is no spatial correlation in the embedding features. Recent work such as https://arxiv.org/abs/1803.01271 show that for most tasks, 1D CNNs outperform recurrent architectures while being faster to train

Paper and discussion history. A mention is not an endorsement.

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling — HN submission

Sun, 19 Aug 2018 04:47:39 GMT

Ask HN: Convolutional VS Recurrent/LSTM/GRU neural networks

Many research found that Convolutional neural networks can reach near the state of art of Recurrent/LSTM/GRU neural networks ON many NLP/Audio process tasks, but with several times training speedup and less memory usage, then why we still use RNN? RNN should have more power on NLP/Audio, but too slow, maybe it has many incredible power we can't explore now, maybe it can reach near human intelligent in the future with more powerful computer power, in that future RNN maybe rediscover and revival like neural network now by DNN, but now can we just use CNN for nearly every thing in real product…

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling — HN submission

Fri, 17 Aug 2018 02:24:14 GMT

An Empirical Evaluation of Generic CNN and RNN for Sequence Modeling

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling — mentioned by visarga

Thu, 28 Jun 2018 16:14:03 GMT

For most applications you can probably use a TCN (temporal convolutional network) instead of LSTM. TCN's are implemented in all major frameworks and work an order of magnitude faster because they are parallel. https://arxiv.org/abs/1803.01271 https://arxiv.org/abs/1608.08242

Paper and discussion history. A mention is not an endorsement.

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling — HN submission

Thu, 15 Mar 2018 14:21:40 GMT

TCNs are better than RNNs

21 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling — HN submission

Wed, 14 Mar 2018 23:25:31 GMT

Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

4 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.