6 papers
Data Augmentation of Time-Series Data in Human Movement Biomechanics: A Scoping Review
Christina Halmich, Lucas Höschler, Christoph Schranz +1
The integration of machine learning and deep learning has transformed data analytics in biomechanics, enabled by extensive wearable sensor data. However, the field faces challenges…
Convolutional Differentiable Logic Gate Networks
Felix Petersen, Hilde Kuehne, Christian Borgelt +2
With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference. Recently, an approach for learning logic gat…
TrAct: Making First-layer Pre-Activations Trainable
Felix Petersen, Christian Borgelt, Stefano Ermon
We consider the training of the first layer of vision models and notice the clear relationship between pixel values and gradient update magnitudes: the gradients arriving at the we…
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms
Felix Petersen, Christian Borgelt, Tobias Sutter +3
When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular appr…
Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
Felix Petersen, Christian Borgelt, Aashwin Mishra +1
We deal with the problem of gradient estimation for stochastic differentiable relaxations of algorithms, operators, simulators, and other non-differentiable functions. Stochastic s…
Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Mishra, Hilde Kuehne +3
We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal appro…