- Abstract: We propose a multiagent system that have feedforward networks as its subset while free from layer structure with matrix-vector scheme. Deep networks are often compared to the brain neocortex or visual perception system. One of the largest difference from human brain is the use of matrix-vector multiplication based on layer architecture. It would help understanding the way human brain works if we manage to develop good deep network model without the layer architecture while preserving their performance. The brain neocortex works as an aggregation of the local level interactions between neurons, which is rather similar to multiagent system consists of autonomous partially observing agents than units aligned in column vectors and manipulated by global level algorithm. Therefore we suppose that it is an effective approach for developing more biologically plausible model while preserving compatibility with deep networks to alternate units with multiple agents. Our method also has advantage in scalability and memory efficiency. We reimplemented Stacked Denoising Autoencoder(SDAE) as a concrete instance with our multiagent system and verified its equivalence with the standard SDAE from both theoritical and empirical perspectives. Additionary, we also proposed a variant of our multiagent SDAE named "Sparse Connect SDAE", and showed its computational advantage with the MNIST dataset.
- TL;DR: We propose a multiagent system that have feed-forward networks as its subset but free from layer scheme.
- Conflicts: weblab.t.u-tokyo.ac.jp, k.u-tokyo.ac.jp, g.ecc.u-tokyo.ac.jp