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20102021
most citedEnsemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks

72 citations · 285 across the 18 of their papers we have counts for

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Showing 2017Show all

5 papers · 1 filter

physics.chem-ph2017★ 15 cited

Neural network approach to time-dependent dividing surfaces in classical reaction dynamics

Philippe Schraft, Andrej Junginger, Matthias Feldmaier +4

In a dynamical system, the transition between reactants and products is typically mediated by an energy barrier whose properties determine the corresponding pathways and rates. The…

stat.ML2017★ 72 cited

Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks

Thilo Strauss, Markus Hanselmann, Andrej Junginger +1

Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable…

physics.chem-ph2017★ 31 cited

Chemical dynamics between wells across a time-dependent barrier: Self-similarity in the Lagrangian descriptor and reactive basins

Andrej Junginger, Lennart Duvenbeck, Matthias Feldmaier +3

In chemical or physical reaction dynamics, it is essential to distinguish precisely between reactants and products for all time. This task is especially demanding in time-dependent…

physics.chem-ph2017★ 37 cited

Obtaining time-dependent multi-dimensional dividing surfaces using Lagrangian descriptors

Matthias Feldmaier, Andrej Junginger, Jörg Main +2

Dynamics between reactants and products are often mediated by a rate-determining barrier and an associated dividing surface leading to the transition state theory rate. This framew…

physics.chem-ph2017★ 4 cited

Variational principle for the determination of unstable periodic orbits and instanton trajectories at saddle points

Andrej Junginger, Jörg Main, Günter Wunner +1

The complexity of arbitrary dynamical systems and chemical reactions, in particular, can often be resolved if only the appropriate periodic orbit - in the form of a limit cycle, di…