most citedOn the regularization of Wasserstein GANs

130 citations · 130 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2026

A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines

Kenneth Martin, Simon Heilig, Asja Fischer +3

Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the availability of many alterna…

stat.ML2026130 cited

On the regularization of Wasserstein GANs

Henning Petzka, Asja Fischer, Denis Lukovnikov

Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems dur…

cs.CL2026

DepthKV: Layer-Dependent KV Cache Pruning for Long-Context LLM Inference

Zahra Dehghanighobadi, Asja Fischer

Long-context reasoning is a critical capability of large language models (LLMs), enabling applications such as long-document understanding, summarization, and code generation. Howe…

cs.SD2024

DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution

Matías Pizarro, Dorothea Kolossa, Asja Fischer

Adversarial attacks can mislead automatic speech recognition (ASR) systems into predicting an arbitrary target text, thus posing a clear security threat. To prevent such attacks, w…

eess.AS2024

Robustifying automatic speech recognition by extracting slowly varying features

Matías Pizarro, Dorothea Kolossa, Asja Fischer

In the past few years, it has been shown that deep learning systems are highly vulnerable under attacks with adversarial examples. Neural-network-based automatic speech recognition…