|Topic:||Exploring Deep Declarative Neural Networks|
|Announce as:||Diplomová práce, Bakalářská práce, Semestrální projekt|
|Description:||In standard feed-forward neural networks, each node computes a function of its input, where the parameters of this function are learnable. As
such, the function computed by these nodes is explicitly defined. Gould et al. recently proposed a novel set of nodes, named declarative
nodes. Rather than explicitly defining a function, the output of a declarative node is a set of parameters that minimizes an optimization
problem. This results in a new and powerful set of capabilities as it allows much more complex computations to happen in nodes. Gould et al.
provide two examples of the use of declarative nodes: robust pooling unaffected by outliers (compared to simple mean pooling) and the
projection onto hyper-spheres. Since deep declarative networks are a very recent concept, the goal of this thesis is to explore their capabilities on a set of illustrative examples.
|Bibliography:||[Gould et al., Deep Declarative Networks: A New Hope, arXiv:1909:04866]|