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publications
The Theory behinds Denoising Diffusion Probabilistic Models
Published in Open Science, 2024
This paper contains the whole theory behinds Denoising Diffusion Probabilistic Models. The code is available in my github repo
Recommended citation: VILHES Samy. (2024). "The Theory behinds Denoising Diffusion Probabilistic Models."Open Science.
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The Theory behinds Variational Auto-Encoders
Published in Open Science, 2024
This paper contains the whole theory behinds Variational Auto-Encoders with some experiments. The code is available in my github repo.
Recommended citation: VILHES Samy. (2023). "The Theory behinds Variational Auto-Encoders."Open Science.
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Understanding Neural Tangent Kernel: Key Theories and Experimental Insights
Published in HAL Open Science, 2024
This paper is about fixing template issue #693.
Recommended citation: VILHES Samy. (2024). "Understanding Neural Tangent Kernel: Key Theories and Experimental Insights." HAL Open Science.
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A control of the false anomaly rate for Anomaly Detections problems on Images
Published in Open Science, 2024
This paper summarizes the initial stages of my thesis research. We focus in a first step on anomaly detection algorithms for image data. I explore various techniques and incorporate a thresholding procedure utilizing p-values, inspired by the work presented in Testing for Outliers with Conformal p-values. The code can be found here: github repo
Recommended citation: VILHES Samy. (2024). "A control of the false anomaly rate for Anomaly Detections problems on Images."Open Science.
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PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection
Published in EUSIPCO 2025, 2025
Time series anomaly detection (TSAD) focuses on identifying whether observations in streaming data deviate significantly from normal patterns. With the prevalence of connected devices, anomaly detection on time series has become paramount, as it enables real-time monitoring and early detection of irregular behaviors across various application domains. In this work, we introduce PatchTrAD, a Patch-based Transformer model for time series anomaly detection. Our approach leverages a Transformer encoder along with the use of patches under a reconstructionbased framework for anomaly detection. Empirical evaluations on multiple benchmark datasets show that PatchTrAD is on par, in terms of detection performance, with state-of-the-art deep learning models for anomaly detection while being time efficient during inference. Github Repo
Recommended citation: Samy-Melwan Vilhes, GILLES Gasso, Mokhtar Z. Alaya (2025). "PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection." EUSIPCO 2025.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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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
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Teaching experience 2
Workshop, University 1, Department, 2015
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