  Representation learning is at the heart of what makes deep learning
effective. In this work, we introduce a new framework for representation
learning that we call "Holographic Neural Architectures" (HNAs). In the same
way that an observer can experience the 3D structure of a holographed object by
looking at its hologram from several angles, HNAs derive Holographic
Representations from the training set. These representations can then be
explored by moving along a continuous bounded single dimension. We show that
HNAs can be used to make generative networks, state-of-the-art regression
models and that they are inherently highly resistant to noise. Finally, we
argue that because of their denoising abilities and their capacity to
generalize well from very few examples, models based upon HNAs are particularly
well suited for biological applications where training examples are rare or
noisy.
