=== Doors open @ 6:30 ===
=== Networking ===
=== Small intro ===
=== Break & Networking ===
=== Talk 1 ===
Sven Giesselbach Data Scientist @ Fraunhofer IAIS with his NIPS paper on transfer learning "Corresponding Projections for Orphan Screening"
We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding afﬁnities of compounds to an orphan protein, i.e., one for which no training data is available. The identiﬁcation of compounds with high afﬁnity is a central concern in medicine since it can be used for drug discovery and design. Given a set of prediction models for proteins with labelled training data and a similarity between the proteins, corresponding projections constructs a model for the orphan protein from them such that the similarity between models resembles the one between proteins. Under the assumption that the similarity resemblance holds, we derive an efﬁcient algorithm for kernel methods. We empirically show that the approach outperforms the state-of-the-art in orphan screening.
=== Talk 2 ===
Sebastian Niehaus - Data Scientist @ AICURA medical
We present a reinforcement learning approach for detecting objects within an image. Our approach performs a step-wise deformation of a bounding box with the goal of tightly framing the object. It uses a hierarchical tree-like representation of predefined region candidates, which the agent can zoom in on. This reduces the number of region candidates that must be evaluated so that the agent can afford to compute new feature maps before each step to enhance detection quality. We compare an approach that is based purely on zoom actions with one that is extended by a second refinement stage to fine-tune the bounding box after each zoom step. We also improve the fitting ability by allowing for different aspect ratios of the bounding box.
=== Networking ===
=== Closing ===