-
I'm getting a method error when trying to run
```julia
using Turing: Variational
m = clamp_sim(trq_m, q_m)
advi = ADVI(10, 1000)
q = vi(m, advi)
```
where `clamp_sim` is a fairly complicated m…
-
# 📚 Documentation/Examples
Hi,
I've been trying to use variational GP (varGP) and sparse GP (spGP) for a data set that is semi-large, approx 20,000 points training.
I've been having some problems…
-
Hi all,
Given Cole Monnahan's success replicating Hamiltonian MCMC into TMB (based on the NUTS algorithm from STAN)), I was wondering if anyone was interested in continuing to explore STAN features w…
-
Hi,
I have been implementing decoupled sampling of GPs and bumped into a rather strange issue. I tried to simplify it by the following piece of code, which is a sparse GP example in which we try to…
-
@stepan-tsirkin It seems that you are implementing Wannierization, i.e. the wannier90.x part, in the wannierise branch, is it right?
Maybe after that feature is implemented, it would be interesting…
-
We allow `model.fit(method="advi")`, but we have largely ignored what happens next for the user. We should improve this situation. We should at least improve two aspects what object we return when us…
-
# During Training
We need to monitor the progress or stability of training right after the end of each `epoch`s. We can consider both quantitative and qualitative evaluations.
### Quantitative …
vrvrv updated
3 years ago
-
SentenceVAE/
│
├── encoder.py
│ ```python
│ import torch
│ from torch import nn
│
│ class SentenceEncoder(nn.Module):
│ '''Sentence Encoder with byte-level BPE tokenization, lear…
-
### Sources
- https://github.com/ZechengLi19/Awesome-Sign-Language
- https://github.com/Skye601/SLR
- https://www.sign-lang.uni-hamburg.de/lrec/project/asllrp.html
- https://www.semanticscholar.…
-
Under this link: https://mc-stan.org/cmdstanr/reference/model-method-variational.html , there are no parameters for "important_resampling", "keep every" in rstan (https://mc-stan.org/rstan/reference/…