Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Pre-training Small LMs
| 7 minute readPre-train small LMs with a limited compute budget.
Site Launch - Accepted paper at EUSIPCO 2024
| less than 1 minute readAnnouncement: first paper accepted as an oral at EUSIPCO
portfolio
Wrong pose
Working pose quality assessment model
CLAP App
A text-prompted sound retrieval model
Criteo Internship
A diffusion-based image background generation project
publications
FICUS: Few-shot Image Classification with Unsupervised Segmentation
Published in EUSIPCO, 2024
This paper tackles ambiguous few-shot image classification problems with unsupervised segmentation.
Recommended citation: Lys, et al. (2024). "FICUS: Few-shot Image Classification with Unsupervised Segmentation." In 2024 32nd European Signal Processing Conference (EUSIPCO)(pp. 1791-1795). IEEE.
Download Paper | Download Slides | Download Bibtex
REVE: A Foundation Model for EEG – Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
Published in NeurIPS, 2025
This paper presents REVE, a foundation model for EEG that generalizes across diverse setups using large-scale pretraining.
Recommended citation: El Ouahidi, et al. (2025). "REVE: A Foundation Model for EEG Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects." In Advances in Neural Information Processing Systems (NeurIPS 2025).
Download Paper | Download Slides | Download Bibtex
TENSLORA: Tensor Alternatives for Low-Rank Adaptation
Published in ICASSP, 2026
This paper introduces TensLoRA, a unified framework for tensor-based low-rank adaptations in Transformers.
Recommended citation: Marmoret, et al. (2025). "TensLoRA: Tensor Alternatives for Low-Rank Adaptation." In 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
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Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training
Published in EUSIPCO, 2026
This paper proposes inference-time inner looping to extend computation in frozen pretrained Transformers without training.
Recommended citation: Lys, et al. (2026). "Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training." In 2026 34th European Signal Processing Conference (EUSIPCO). IEEE.
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Residual Connections and the Causal Shift: Uncovering a Structural Misalignment in Transformers
Published in EUSIPCO, 2026
This paper identifies a residual-path input-output alignment shift in autoregressive Transformers and proposes residual attenuation to mitigate it.
Recommended citation: Lys, et al. (2026). "Residual Connections and the Causal Shift: Uncovering a Structural Misalignment in Transformers." In 2026 34th European Signal Processing Conference (EUSIPCO). IEEE.
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D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding
Published in arXiv, 2026
This paper introduces D5P4, a beam-style decoding method for parallel discrete diffusion that balances quality and diversity.
Recommended citation: Lys, et al. (2026). "D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding." arXiv preprint arXiv:2603.19146.
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How Transferable Are EEG Foundation Models? A Case Study on Sleep Staging
Published in EUSIPCO, 2026
This paper evaluates the transferability of EEG foundation models using sleep staging as a case study.
Recommended citation: Lamouroux, et al. (2026). "How Transferable Are EEG Foundation Models? A Case Study on Sleep Staging." In 2026 34th European Signal Processing Conference (EUSIPCO). IEEE.
Key Ingredients for EEG Foundation Models: What Works - and What Doesn’t
Published in EUSIPCO, 2026
This paper analyzes the core methodologies and architectural choices behind EEG foundation models to identify what works and what doesn’t.
Recommended citation: Lioi, et al. (2026). "Key Ingredients for EEG Foundation Models: What Works - and What Doesn't." In 2026 34th European Signal Processing Conference (EUSIPCO). IEEE.
talks
Talk 1 on Relevant Topic in Your Field
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.