5 papers
KV Cache Compression Through the Lens of Transform Coding
Hannah Laus, Claudio Mayrink Verdun, Hao Wang +2
The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods address this bottleneck by re…
Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier
Berk Hayta, Hannah Laus, Simon Mittermaier +1
Real-world sensor-based learning systems require uncertainty estimation that is both reliable and computationally efficient. Evidential Deep Learning (EDL) provides single-pass unc…
Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
Hannah Laus, Suzanna Parkinson, Vasileios Charisopoulos +2
Machine learning methods are commonly used to solve inverse problems, wherein an unknown signal must be estimated from few indirect measurements generated via a known acquisition p…
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus +2
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional regression or learning problems to increase the confidence of a given predictor. We devel…
Uncertainty quantification for learned ISTA
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer +2
Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretabil…