7 accepted papers
Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates with probabilities proportional to a given reward. However, GFlowNets can only be used with a predefined scalar reward, which can be either computationally expensive or not directly accessible…
"Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the field of machine learning. While a pre-trained vision-language model like CLIP has demonstrated remarkable zero-shot performance, further adaptation of the model…
Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precision networks achieve better performance than networks with homogeneous bitwidth for the same size constraint. Since choosi…
Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying…
Deep neural networks (DNN) are powerful models for many pattern recognition tasks, yet they tend to have many layers and many neurons resulting in a high computational complexity. This limits their application to high-performance computing platforms. In order to evaluate a trained DNN on a lower-per…
Deep neural networks (DNN) achieve very good performance in many machine learning tasks, but are computationally very demanding. Hence, there is a growing interest on model reduction methods for DNN. Model reduction allows to reduce the number of computations needed to evaluate a trained DNN without…
This paper presents a new supervised approach to extract the power trace of individual loads from single channel aggregate power signals in non-intrusive load monitoring (NILM) systems. Recent approaches to this source separation problem are based on factorial hidden Markov models (FHMM). Drawbacks…