Papers

We will discuss two releated papers:

• Towards Generalisable Time Series Understanding Across Domains
• Are foundation models useful feature extractors for electroencephalography analysis?

Towards Generalisable Time Series Understanding Across Domains

Recent breakthroughs in natural language processing and computer vision, driven by efficient pre-training on large datasets, have enabled foundation models to excel on a wide range of tasks. However, this potential has not yet been fully realised in time series analysis, as existing methods fail to address the heterogeneity in large time series corpora. Prevalent in domains ranging from medicine to finance, time series vary substantially in characteristics such as variate count, inter-variate relationships, temporal patterns, and sampling frequency. To address this, we introduce a novel pre-training paradigm specifically designed to handle time series heterogeneity. We propose a tokeniser with learnable domain signatures, a dual masking strategy, and a normalised cross-correlation loss, enabling our open model for general time series analysis (OTiS) to efficiently learn from large time series corpora. Extensive benchmarking on diverse tasks, such as classification, regression, and forecasting, demonstrates that OTiS outperforms state-of-the-art baselines.

Are foundation models useful feature extractors for electroencephalography analysis?

The success of foundation models in natural language processing and computer vision has motivated similar approaches for general time series analysis. While these models are effective for a variety of tasks, their applicability in medical domains with limited data remains largely unexplored. To address this, we investigate the effectiveness of foundation models in medical time series analysis involving electroencephalography (EEG). Through extensive experiments on tasks such as age prediction, seizure detection, and the classification of clinically relevant EEG events, we compare their diagnostic accuracy with that of specialised EEG models. Our analysis shows that foundation models extract meaningful EEG features, outperform specialised models even without domain adaptation, and localise task-specific biomarkers. Moreover, we demonstrate that diagnostic accuracy is substantially influenced by architectural choices such as context length. Overall, our study reveals that foundation models with general time series understanding eliminate the dependency on large domain-specific datasets, making them valuable tools for clinical practice.

Download:

• Towards Generalisable Time Series Understanding Across Domains
• Are foundation models useful feature extractors for electroencephalography analysis?

Format

• Paper shared 1 week ahead for pre‑reading
• 5–10 min summary by a volunteer (no slides — we’ll scroll through the PDF together)
• Open discussion on methodology, strengths/weaknesses, clinical relevance, and future directions
• Papers chosen collaboratively to balance foundational ML research and cutting‑edge medical AI
• Who should attend? Researchers, clinicians, students — anyone curious about ML/AI in medicine.
• How to join? Simply show up (no registration required). Looking forward to seeing you there!

Organizers

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