activity
20182023
most citedScalable Deep Learning for RNA Secondary Structure Prediction

9 citations · 16 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Towards Automated Design of Riboswitches

Frederic Runge, Jörg K. H. Franke, Frank Hutter

Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the num…

cs.LG2023★ 9 cited

Scalable Deep Learning for RNA Secondary Structure Prediction

Jörg K. H. Franke, Frederic Runge, Frank Hutter

The field of RNA secondary structure prediction has made significant progress with the adoption of deep learning techniques. In this work, we present the RNAformer, a lean deep lea…

cond-mat.mtrl-sci2023★ 3 cited

A Digital Twin to overcome long-time challenges in Photovoltaics

Larry Lüer, Marius Peters, Ana Sunčana Smith +9

The recent successes of emerging photovoltaics (PV) such as organic and perovskite solar cells are largely driven by innovations in material science. However, closing the gap to co…

cs.LG2019★ 4 cited

Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control

Jörg K. H. Franke, Gregor Köhler, Noor Awad +1

Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures. We propose a novel approach to automatically find strong topologies f…

cs.CL2018

Robust and Scalable Differentiable Neural Computer for Question Answering

Jörg Franke, Jan Niehues, Alex Waibel

Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network,…