activity
20182020
most citedEvolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

5 citations · 5 across the 1 of their papers we have counts for

collaborators

6 papers

cs.NE20205 cited

Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Tim Cofala, Lars Elend, Philip Mirbach +3

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SA…

cs.LG2020

Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent

Jonas Prellberg, Oliver Kramer

In deep multi-task learning, weights of task-specific networks are shared between tasks to improve performance on each single one. Since the question, which weights to share betwee…

cs.CV2019

Acute Lymphoblastic Leukemia Classification from Microscopic Images using Convolutional Neural Networks

Jonas Prellberg, Oliver Kramer

Examining blood microscopic images for leukemia is necessary when expensive equipment for flow cytometry is unavailable. Automated systems can ease the burden on medical experts fo…

cs.NE2018

Limited Evaluation Evolutionary Optimization of Large Neural Networks

Jonas Prellberg, Oliver Kramer

Stochastic gradient descent is the most prevalent algorithm to train neural networks. However, other approaches such as evolutionary algorithms are also applicable to this task. Ev…

cs.NE2018

Lamarckian Evolution of Convolutional Neural Networks

Jonas Prellberg, Oliver Kramer

Convolutional neural networks belong to the most successul image classifiers, but the adaptation of their network architecture to a particular problem is computationally expensive.…

cs.CV2018

Multi-label Classification of Surgical Tools with Convolutional Neural Networks

Jonas Prellberg, Oliver Kramer

Automatic tool detection from surgical imagery has a multitude of useful applications, such as real-time computer assistance for the surgeon. Using the successful residual network…