258 citations · 1.2k across the 64 of their papers we have counts for
6 papers · 1 filter
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection…
Efficiently Approximating Attention Is Hard
Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno +2
Softmax attention is ubiquitous in modern machine learning, but its quadratic scaling with sequence length makes it costly. To reduce this cost, attention is often approximated wit…
CENDRe: Concept Extraction with Natural Domain Representations
Antonia Holzapfel, Andres Felipe Posada Moreno, Sebastian Trimpe
Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical domains requires understanding the temporal and spectral patte…
The risk of KV cache compression
Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno +2
Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compres…
Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices
Alexander Gräfe, Ding Huo, Vincent de Bakker +3
Transformer models are rapidly becoming a cornerstone of modern Internet of Things (IoT) applications, yet their computational and memory demands far exceed the capabilities of a s…
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr +3
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. Howeve…