Mar 11, 2024 · Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how R3SL pre- ...
Mar 11, 2024 · In SimCLR, the learning objective is to map a data point and its augmentation(s) to similar representations, while pushing different data points ...
Mar 11, 2024 · RS3L, a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation for contrastive learning, ...
Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how R3SL pre-training enables ...
Oct 2, 2024 · We propose RS3L ("Re-simulation-based self-supervised representation learning"), a novel simulation-based SSL strategy that employs a method of ...
Mar 11, 2024 · Introduces RS3L, a novel Self-Supervised Learning strategy leveraging re-simulation for data augmentation in contrastive learning, ...
Mar 13, 2024 · - The authors focus on high-energy physics experiments and use RS3L for pre-training to improve the performance of downstream tasks such as ...
Mar 18, 2024 · With our method, the manifold is discerned through the training procedure, while the latent evolution due to Ricci flow induces a more ...
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Dec 13, 2024 · This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for ...
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of ...