Well the whole "deep learning" revolution really only started around 2012 when the ImageNet competition was won with a neural net. There have been numerous small breakthroughs that collectively have made deep neural…
Scaled-ELU is meant to create self-normalizing nets, but that's nothing you can't achieve a bit less efficiently with explicit normalization (batch norm, etc), so hardly a game changer.
I don't now about radically different.. after all a graph is just a bunch of nodes, edges and associated attributes. There are standard alternatives to the DOT/GV graph description language though, such as GraphML (XML…
Well the whole "deep learning" revolution really only started around 2012 when the ImageNet competition was won with a neural net. There have been numerous small breakthroughs that collectively have made deep neural…
Scaled-ELU is meant to create self-normalizing nets, but that's nothing you can't achieve a bit less efficiently with explicit normalization (batch norm, etc), so hardly a game changer.
I don't now about radically different.. after all a graph is just a bunch of nodes, edges and associated attributes. There are standard alternatives to the DOT/GV graph description language though, such as GraphML (XML…