Thursday, 12th of September

14h30 (room R2014, 660 building) (see location)

2 talks

Victor Berger

(TAU)

From abstract items to latent space to observed data and back: Compositional Variational Auto-Encoder

Conditional Generative Models are now acknowledged an essential
tool in Machine Learning. This paper focuses on their control.
While many approaches aim at disentangling the data through the
coordinate-wise control of their latent representations, another
direction is explored in this paper. The proposed CompVAE handles
data with a natural multi-ensemblist structure (i.e. that can naturally
be decomposed into elements). Derived from Bayesian variational
principles, CompVAE learns a latent representation leveraging both
observational and symbolic information. A first contribution of the
approach is that this latent representation supports a
compositional generative model, amenable to multi-ensemblist
operations (addition or subtraction of elements in the composition).
This compositional ability is enabled by the invariance and generality
of the whole framework w.r.t. respectively, the order and number of the
elements. The second contribution of the paper is a proof of concept on
synthetic 1D and 2D problems, demonstrating the efficiency of the
proposed approach.

Zhengyin Liu

(TAU)

Discovery Challenges

Contact: guillaume.charpiat at inria.fr
All TAU seminars: here