Comments (1)
shreydesai
commented on September 17, 2024
It may be useful to extract n-gram features from the text first. Traditional convolutional networks are well suited for this task – see Kim's paper on this. Many models will usually extract features using a convolutional network, then use a LSTM/GRU to learn sequential dependencies among the features. TCNs can be thought of as a drop-in replacement for the recurrent networks.
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Related Issues (20)
- 函数调用问题
- LSTM and RNN used and issues of compatibility HOT 1
- issue about Input of TCN HOT 1
- ModuleNotFoundError: No module named 'tcn' HOT 2
- Clarification on figure 3(a) HOT 4
- Training on variable-length sequences HOT 1
- copy memory questions
- why raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enabled
- seq2seq
- How should I choose correct layers number?
- How to save model?
- Code Question about: input the final conv-layer output to the linear layer
- What is the accuracy supposed to be for the MNIST problem?
- Is TCN suitable for spatio-temporal data? HOT 7
- why?
- Correlate .mat files with songs in Nottingham dataset
- Zero padding - possibly incorrect behavior? HOT 1
- DDP training with TCN Model
- do you have code examples for multivariate time series
- loss=nan
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