1 research outputs found
Deep Compression of Sum-Product Networks on Tensor Networks
Sum-product networks (SPNs) represent an emerging class of neural networks
with clear probabilistic semantics and superior inference speed over graphical
models. This work reveals a strikingly intimate connection between SPNs and
tensor networks, thus leading to a highly efficient representation that we call
tensor SPNs (tSPNs). For the first time, through mapping an SPN onto a tSPN and
employing novel optimization techniques, we demonstrate remarkable parameter
compression with negligible loss in accuracy