1 citations · 1 across the 6 of their papers we have counts for
6 papers
Inverse Neural Operator for ODE Parameter Optimization
Zhi-Song Liu, Wenqing Peng, Helmi Toropainen +5
We propose the Inverse Neural Operator (INO), a two-stage framework for recovering hidden ODE parameters from sparse, partial observations. In Stage 1, a Conditional Fourier Neural…
Uncertainty Reasoning with Photonic Bayesian Machines
F. Brückerhoff-Plückelmann, H. Borras, S. U. Hulyal +12
Artificial intelligence (AI) systems increasingly influence safety-critical aspects of society, from medical diagnosis to autonomous mobility, making uncertainty awareness a centra…
Accelerated Execution of Bayesian Neural Networks using a Single Probabilistic Forward Pass and Code Generation
Bernhard Klein, Falk Selker, Hendrik Borras +3
Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in s…
Scalable and Efficient Intra- and Inter-node Interconnection Networks for Post-Exascale Supercomputers and Data centers
Joaquin Tarraga-Moreno, Daniel Barley, Francisco J. Andujar Munoz +5
The rapid growth of data-intensive applications such as generative AI, scientific simulations, and large-scale analytics is driving modern supercomputers and data centers toward in…
Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations
Xiao Wang, Hendrik Borras, Bernhard Klein +1
The disparity between the computational demands of deep learning and the capabilities of compute hardware is expanding drastically. Although deep learning achieves remarkable perfo…
On Hardening DNNs against Noisy Computations
Xiao Wang, Hendrik Borras, Bernhard Klein +1
The success of deep learning has sparked significant interest in designing computer hardware optimized for the high computational demands of neural network inference. As further mi…