Motion Invariance in Visual Environments

The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as happens in nature. In this paper, we claim that their processing naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of visual learning based on convolutional features. The theory addresses a number of intriguing questions that arise in natural vision, and offers a well-posed computational scheme for the discovery of convolutional filters over the retina. They are driven by the Euler-Lagrange differential equations derived from the principle of least cognitive action, that parallels laws of mechanics. Unlike traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario in which feature learning takes place by unsupervised processing of video signals. An experimental report of the theory is presented where we show that features extracted under motion invariance yield an improvement that can be assessed by measuring information-based indexes.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0711

Chapter Amori proibiti in palazzo Mondragone Carnesecchi. Un’inedita cupola di Antonio Puglieschi e una memoria medicea

The recent discovery of a dome painted by Antonio Puglieschi in the Mondragone Carnesecchi palace, representing Mars and Venus surprised by Vulcan, offers the occasion to reflect on the symbols used by Francesco I de’ Medici and Bianca Cappello. According to an ancient rumor, the clandestine love encounters between Francesco and Bianca had taken place in that very same palace and the depiction of the mythological infidelity may alluded to that relationship. The paper analyses different sources which show that Francesco was often portrayed as Mars and Bianca as Venus: specific attention is given to the detailed description of their wedding celebrations which revolved around the theme of the meeting and love of Venus and Mars. It’s possible that the Del Vernaccia family, who owned the palace and commissioned the painting to Antonio Puglieschi, wanted to remember and somehow celebrate events that took place in those rooms more than a century before

Backprop Diffusion is Biologically Plausible

The Backpropagation algorithm relies on the abstraction of using a neural model that gets rid of the notion of time, since the input is mapped instantaneously to the output. In this paper, we claim that this abstraction of ignoring time, along with the abrupt input changes that occur when feeding the training set, are in fact the reasons why, in some papers, Backprop biological plausibility is regarded as an arguable issue. We show that as soon as a deep feedforward network operates with neurons with time-delayed response, the backprop weight update turns out to be the basic equation of a biologically plausible diffusion process based on forward-backward waves. We also show that such a process very well approximates the gradient for inputs that are not too fast with respect to the depth of the network. These remarks somewhat disclose the diffusion process behind the backprop equation and leads us to interpret the corresponding algorithm as a degeneration of a more general diffusion process that takes place also in neural networks with cyclic connections.Comment: 9 pages, 3 figures. arXiv admin note: text overlap with arXiv:1907.0510