Contents
Why is FNN better than RNN?
Note that the “degree of freedom” can be extended by time delay embedding of states[2] while keeping the same number of hidden units. Therefore, RNN is actually compressing the previous memory information with loss by doing convolution, while FNN-TD is just exposing them in a sense with no loss of memory information.
Why we used long short term memory LSTM networks instead of RNN?
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.
Is BERT faster than LSTM?
Given the same resource and time, the pretrained BERT perfomed slightly better than LSTM but no significant difference. Potentially, training the BERT model from scratch on similar tweets could produce much better result, while the required resources and cost is beyond this study.
What are the advantages of BERT?
Trained on 2.5 billion words, its main advantage is its use of bi-directional learning to gain context of words from both left to right context and right to left context simultaneously, BERT’s bidirectional training approach is optimized for predicting masked words (Masked LM) and outperforms left-to-right training …
Why does the transformer do better than RNN?
The first point is the main reason why transformer do not suffer from long dependency issues. Original transformers do not relies on past hidden states to capture dependencies with previous words, they process a sentence as a whole, reason why there is no risk to loose (or ‘forget’) past information.
Why does the transformer do better than LSTM?
The point is that the encoding of a specific word is retained only for the next time step, which means that the encoding of a word strongly affect only the representation of the next word, its influence is quickly lost after few time steps.
Why is RNN unfit for long-range memory?
This makes RNN unfit even with technologies like CuDNN which slow down the whole process for GPU. 2. The second is the long-range dependencies. We know that, theoretically, LSTMs can possess long-term memory, yet memorizing things for a long period of time is a challenge. There is another problem that I will explain with an example.
Is there a difference between LSTM and RNN?
No question LSTM and GRU and derivatives are able to learn a lot of longer term information! See results here; but they can remember sequences of 100s, not 1000s or 10,000s or more. And one issue of RNN is that they are not hardware friendly.