Section: Research Program
Deep Learning is at the root of quite a few breakthroughs in machine learning and sequential decision making, albeit requiring gigantic resources . Some reasons for these performance jumps are clear (more data, more computational power, more complex search space). Still, the nature of the dynamical system made of training a deep NN yet remains an open question, at the crossroad of information geometry and non-convex optimization. A related open question concerns the neural architecture design. Deep Learning recent developments regarding generative adversarial networks  and domain adaptation  are relevant to optimal design applications.
The challenges addressed by TAU range from theoretical ML issues (characterization of learnable problems w.r.t the ratio of the data size/neural architecture size) to functional issues (how to encode information invariance and deal with higher order logic beyond convolutional architectures) to societal issues (how to open the black-box of a deep NN and ensure the fairness of the process).
The TAU team has a unique international expertise in three aspects relevant to deep learning, respectively regarding Riemannian geometry ,  (in order to efficiently navigate in the search manifold), statistical physics  (to apprehend the learnability region as the architecture size goes to infinity with the data size), and Genetic Programming  and neuro-evolution (that provide original avenues for DNN architecture learning). Related industrial contracts involve ADAMME (FUI 2016) and RTE (Energy Management).