15 citations · 66 across the 10 of their papers we have counts for
4 papers · 1 filter
Hierarchical Conflict Propagation: Sequence Learning in a Recurrent Deep Neural Network
Andrew J. R. Simpson
Recurrent neural networks (RNN) are capable of learning to encode and exploit activation history over an arbitrary timescale. However, in practice, state of the art gradient descen…
Deep Transform: Error Correction via Probabilistic Re-Synthesis
Andrew J. R. Simpson
Errors in data are usually unwelcome and so some means to correct them is useful. However, it is difficult to define, detect or correct errors in an unsupervised way. Here, we trai…
Abstract Learning via Demodulation in a Deep Neural Network
Andrew J. R. Simpson
Inspired by the brain, deep neural networks (DNN) are thought to learn abstract representations through their hierarchical architecture. However, at present, how this happens is no…
Over-Sampling in a Deep Neural Network
Andrew J. R. Simpson
Deep neural networks (DNN) are the state of the art on many engineering problems such as computer vision and audition. A key factor in the success of the DNN is scalability - bigge…