9 papers · 1 filter
Be Wary of Your Time Series Preprocessing
Sofiane Ennadir, Tianze Wang, Oleg Smirnov +2
Normalization and scaling are fundamental preprocessing steps in time series modeling, yet their role in Transformer-based models remains underexplored from a theoretical perspecti…
Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects
Levente Zólyomi, Levente Zólyomi, Tianze Wang +3
The proliferation of digital interactions across diverse domains, such as healthcare, e-commerce, gaming, and finance, has resulted in the generation of vast volumes of event strea…
Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift
Tianze Wang, Sofiane Ennadir, John Pertoft +7
Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial se…
If You Want to Be Robust, Be Wary of Initialization
Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis +1
Graph Neural Networks (GNNs) have demonstrated remarkable performance across a spectrum of graph-related tasks, however concerns persist regarding their vulnerability to adversaria…
Enhancing Graph Classification Robustness with Singular Pooling
Sofiane Ennadir, Oleg Smirnov, Yassine Abbahaddou +2
Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remain…
Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models
Sofiane Ennadir, Levente Zólyomi, Oleg Smirnov +4
Transformer models have become the dominant backbone for sequence modeling, leveraging self-attention to produce contextualized token representations. These are typically aggregate…