mfkasim1

3 ms·

1. 1.▲
   by mfkasim1 3y ago

   It's here: https://github.com/machine-discovery/deer :)
2. 2.▲
   by mfkasim1 3y ago

   Yes, sorry for that. I'm coming from physics, so L for sequence length and n for the number of dimensions make more sense :D I agree with the cubic time and quadratic space is a big limitation for now and I'm looking for ways to m
3. 3.▲
   by mfkasim1 3y ago

   We're preparing (i.e. cleaning up) the code for the repo. Will update you when we release the code.
4. 4.▲
   by mfkasim1 3y ago

   Yes, there is no convergence guarantee, but what we found is that typical untrained RNN units (e.g., GRU, LSTM, or a simple MLP for NeuralODE) can converge within 3-5 iterations which gives them a huge speed up over the sequential method. T
5. 5.▲
   by mfkasim1 3y ago

   The author of the paper here. The cubic time and quadratic space complexity is with respect to the number of dimensions (n), not the sequential time steps. The time and space complexity w.r.t. the number of time steps (L) are both linear, s