You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In our recent paper with @Malezieux, we achieved faster performances with almost no performance drop with a torch-based algorithm.
It would be nice to implement such algorithm in alphcsc, with an optional dependency on pytorch.
Where to add this algorithm is not totally straightforward. I think the easier would probably be to implement a separate algorithm similar to _batch_learn and _online_learn. But another possibility would be to refactor the code to better take into account the bi-level optimization framework (less alternate minimization), which would ease this integration (and follow up that will unfold from our work on the field).
The text was updated successfully, but these errors were encountered:
In our recent paper with @Malezieux, we achieved faster performances with almost no performance drop with a torch-based algorithm.
It would be nice to implement such algorithm in
alphcsc
, with an optional dependency onpytorch
.Where to add this algorithm is not totally straightforward. I think the easier would probably be to implement a separate algorithm similar to
_batch_learn
and_online_learn
. But another possibility would be to refactor the code to better take into account the bi-level optimization framework (less alternate minimization), which would ease this integration (and follow up that will unfold from our work on the field).The text was updated successfully, but these errors were encountered: