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ORCIDJohannes Zenn
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2020 – today
- 2024
[c2]Johannes Zenn, Robert Bamler:
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution. ICML 2024- [i6]Johannes Zenn, Robert Bamler:
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution. CoRR abs/2405.14840 (2024) - [i5]Johannes Zenn, Dominik Gond, Fabian Jirasek, Robert Bamler:
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties. CoRR abs/2406.08075 (2024) - 2023
- [c1]Johannes Zenn, Robert Bamler:
Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers. Tiny Papers @ ICLR 2023 - [i4]Johannes Zenn, Robert Bamler:
Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers. CoRR abs/2304.14390 (2023) - [i3]Tim Z. Xiao, Johannes Zenn, Robert Bamler:
Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models. CoRR abs/2310.19653 (2023) - [i2]Tim Z. Xiao, Johannes Zenn, Robert Bamler:
The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch. CoRR abs/2312.02168 (2023) - 2021
- [i1]Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig:
ProbNum: Probabilistic Numerics in Python. CoRR abs/2112.02100 (2021)
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