3 citations · 6 across the 6 of their papers we have counts for
6 papers
A Proximal Operator for Inducing 2:4-Sparsity
Jonas M Kübler, Yu-Xiang Wang, Shoham Sabach +5
Recent hardware advancements in AI Accelerators and GPUs allow to efficiently compute sparse matrix multiplications, especially when 2 out of 4 consecutive weights are set to zero.…
Uncertainty separation via ensemble quantile regression
Navid Ansari, Hans-Peter Seidel, Vahid Babaei
This paper introduces a novel and scalable framework for uncertainty estimation and separation with applications in data driven modeling in science and engineering tasks where reli…
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics
Kevin Tirta Wijaya, Navid Ansari, Hans-Peter Seidel +1
Data-driven generation of molecules with desired properties, also known as inverse molecular design (IMD), has attracted significant attention in recent years. Despite the signific…
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization
Navid Ansari, Alireza Javanmardi, Eyke Hüllermeier +2
Bayesian optimization (BO) provides a powerful framework for optimizing black-box, expensive-to-evaluate functions. It is therefore an attractive tool for engineering design proble…
Autoinverse: Uncertainty Aware Inversion of Neural Networks
Navid Ansari, Hans-Peter Seidel, Nima Vahidi Ferdowsi +1
Neural networks are powerful surrogates for numerous forward processes. The inversion of such surrogates is extremely valuable in science and engineering. The most important proper…
Mixed Integer Neural Inverse Design
Navid Ansari, Hans-Peter Seidel, Vahid Babaei
In computational design and fabrication, neural networks are becoming important surrogates for bulky forward simulations. A long-standing, intertwined question is that of inverse d…