7 citations · 9 across the 4 of their papers we have counts for
4 papers
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…
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models
Johannes Getzner, Bertrand Charpentier, Stephan Günnemann
Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have…
Training, Architecture, and Prior for Deterministic Uncertainty Methods
Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann
Accurate and efficient uncertainty estimation is crucial to build reliable Machine Learning (ML) models capable to provide calibrated uncertainty estimates, generalize and detect O…
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…