Dear authors,

Any idea when to release the code on github?

Regarding the code you release on OpenReview

The main iteration step should be
u = D_inv * b + D_inv * W * u + lambda * (u - (D * u).sum(0) / D.sum())

Below, I provide the reimplemented results for MNIST, with optimal parameter $\lambda=0.2$ as provided in Table 5 in the paper. However, I have doubt with the parameter $\epsilon$ when calculating the weight matrix, $w_{ij}=\exp(-4|x_{i}-x_{j}|^{2}/d_{K}(x_{i})^{2})+\epsilon$. In the paper $\epsilon$ is set to $1$.

However, for Poisson Learning the result is far better when $\epsilon=0$, even better than V-Poisson. Since the original Poisson learning paper does not have this parameter $\epsilon$, I wonder why the authors add $\epsilon$ and doubt if it is a fair comparison with Poisson Learning.