4 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
Johanna Sommer, John Rachwan, Nils Fleischmann +2
Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of network evaluations. This inferen…
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
Xun Wang, John Rachwan, Stephan Günnemann +1
Neural network pruning serves as a critical technique for enhancing the efficiency of deep learning models. Unlike unstructured pruning, which only sets specific parameters to zero…
On the Robustness and Anomaly Detection of Sparse Neural Networks
Morgane Ayle, Bertrand Charpentier, John Rachwan +3
The robustness and anomaly detection capability of neural networks are crucial topics for their safe adoption in the real-world. Moreover, the over-parameterization of recent netwo…
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
John Rachwan, Daniel Zügner, Bertrand Charpentier +3
Pruning, the task of sparsifying deep neural networks, received increasing attention recently. Although state-of-the-art pruning methods extract highly sparse models, they neglect…