3 citations · 8 across the 15 of their papers we have counts for
17 papers
Considering Context: When World Models Need Context Encoders
Oleg Smirnov, Sofiane Ennadir, John Pertoft +2
Methods for generalization in model-based reinforcement learning typically assume that an agent cannot recover the latent context governing the environment dynamics from its own ex…
Stochasticity in Tokenisation Improves Robustness
Sophie Steger, Rui Li, Sofiane Ennadir +4
The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation of the input indicate that m…
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…
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…
Joint Embeddings Go Temporal
Sofiane Ennadir, Siavash Golkar, Leopoldo Sarra
Self-supervised learning has seen great success recently in unsupervised representation learning, enabling breakthroughs in natural language and image processing. However, these me…
Efficient Data Selection for Training Genomic Perturbation Models
George Panagopoulos, Johannes F. Lutzeyer, Sofiane Ennadir +2
Genomic studies face a vast hypothesis space, while interventions such as gene perturbations remain costly and time-consuming. To accelerate such experiments, gene perturbation mod…
Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects
Levente Zólyomi, Tianze Wang, Sofiane Ennadir +2
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…
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell +6
Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning…
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns…
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect
Guokan Shang, Hadi Abdine, Yousef Khoubrane +9
We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruct…
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to advers…
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs…
Approximating Score-based Explanation Techniques Using Conformal Regression
Amr Alkhatib, Henrik Boström, Sofiane Ennadir +1
Score-based explainable machine-learning techniques are often used to understand the logic behind black-box models. However, such explanation techniques are often computationally e…
Interpretable Graph Neural Networks for Tabular Data
Amr Alkhatib, Sofiane Ennadir, Henrik Boström +1
Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing featur…