5 accepted papers
Sparse regularization techniques are well-established in machine learning, yet their application in neural networks remains challenging due to the non-differentiability of penalties like the $L_1$ norm, which is incompatible with stochastic gradient descent. A promising alternative is shallow weight…
Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs. However, to make them useful in high-stakes simulation scenar…
The complexity of black-box algorithms can lead to various challenges, including the introduction of biases. These biases present immediate risks in the algorithms’ application. It was, for instance, shown that neural networks can deduce racial information solely from a patient's X-ray scan, a task…
Because of their large workspace, robot manipulators have the potential to be used for high precision non-contact manufacturing processes, such as laser cutting or welding, on large complex work pieces. However, most industrial manipulators are not able to provide the necessary accuracy requirements…
A data dissemination scenario is considered. The transmitter harvests energy from the environment and uses it to transmit individual data to multiple receivers. We consider a realistic scenario in which only causal knowledge regarding the energy harvesting, the channel fading and the data arrival pr…