The discovery of unique treatment candidates for complex diseases is a challenging task for current drug discovery programs. Biopharma research has developed automated and scalable screening assays of cell culture models to screen thousands of drug candidates in parallel, e.g., by considering bio-image based assays. However, the large amount of data hinders a systematic review by human experts to distinguish between different disease and healthy phenotypes. A prevalent approach to uncover phenotypic endpoints in a dataset is based on the concept of archetypal analysis which seeks for extremal points in a dataset. State-of-the-art non-linear archetypal methods based on variational autoencoders require k - 1 latent dimensions to encode k archetypes. However, in high content imaging we frequently require a significantly larger number of latent dimensions than archetypes to encode HCIs which results in weak latent representations and ambiguous archetypes. To overcome this limitation, we propose to relax the simplex constraint in the latent space to a unit hypersphere and learn the respective archetypes based on online dictionary learning. Extensive experiments on two industry-relevant assays and a synthetic MNIST example demonstrate that our method outperforms state-of-the-art deep archetypal analysis approaches.

Abstract Archetypes represent extreme manifestations of a population with respect to specific characteristic traits or features. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. As mixing of archetypes is performed directly on the input data, linear Archetypal Analysis requires additivity of the input, which is a strong assumption unlikely to hold e.g. in case of image data. To address this problem, we propose learning an appropriate latent feature space while simultaneously identifying suitable archetypes. We thus introduce a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a deep variational information bottleneck and an optimal representation, together with the archetypes, can be learned end-to-end. Moreover, the information bottleneck framework allows for a natural incorporation of arbitrarily complex side information during training. As a consequence, learned archetypes become easily interpretable as they derive their meaning directly from the included side information. Applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. By using different kinds of side information we demonstrate how identified archetypes, along with their interpretation, largely depend on the side information provided.

Symmetry transformations induce invariances and are a crucial building block of modern machine learning algorithms. In many complex domains, such as the chemical space, invariances can be observed, yet the corresponding symmetry transformation cannot be formulated analytically. We propose to learn the symmetry transformation with a model consisting of two latent subspaces, where the first subspace captures the target and the second subspace the remaining invariant information. Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser. Unlike previous methods, we focus on the challenging task of minimising mutual information in continuous domains. To this end, we base the calculation of mutual information on correlation matrices in combination with a bijective variable transformation. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on artificial and molecular datasets.

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter. Existing approaches to this problem are limited by making strong a-priori assumptions about the object’s geometric structure and require seg- mented training data for learning. In this paper, we propose a novel framework for learning hierarchical compositional models (HCMs) which do not suffer from the mentioned limitations. We present a generalized formulation of HCMs and describe a greedy structure learning framework that consists of two phases: Bottom-up part learning and top-down model composition. Our framework integrates the foreground-background segmentation problem into the structure learning task via a background model. As a result, we can jointly optimize for the number of layers in the hierarchy, the number of parts per layer and a foreground- background segmentation based on class labels only. We show that the learned HCMs are semantically meaningful and achieve competitive results when compared to other generative object models at object classification on a standard transfer learning dataset.