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Kristof T Schütt

2 accepted papers

2024

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

ICLR 2024poster

Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptimal performance on large molecular structures and limited training data. To address this gap, we explore the design space…

Cited by 24SourcePDFScholar
2018

Learning how to explain neural networks: PatternNet and PatternAttribution

ICLR 2018poster

DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on multi-layer networks with millions of parameters. This is a cause for concern since linea…

Cited by 433SourcePDFScholar