Recreate a new abstract from the following:
"Machine learning (ML) has become an increasingly important tool in scientific research, allowing researchers to analyze large datasets and make accurate predictions about complex systems. However, understanding why and how the machine makes these predictions is crucial. The field of interpretable ML has developed different approaches to tackle the issue which benefit from the models provided by physics. The known physical models allow for testing the learning of such methods in a controlled scenario, while these methods promise to uncover the underlying parameters of the physical system.

Another example of such mutual relationship with ML is statistical physics. While statistical physics plays a key role in some of the most important discoveries in ML, the study of stochastic processes with ML has had a huge success. In experimental conditions where data is often scarce and noisy, ML methods have outperformed traditional approaches that rely on having a big number of samples. A prominent and timely example of this is the study of molecular diffusion from data arising from single particle tracking techniques [NAME_1 NAME_2], for which extensive ML methods have been developed [XX].
  
  In this work, rather than focusing on accurately predicting pre-labeled diffusive properties, we aim to use interpretable ML methods to extract, in an unsupervised form, the relevant physical parameters of a given set of trajectories.  To do so, we consider the well-known $\beta$-variational autoencoder ($\beta$-VAE) architecture and extend it such as to properly account for the typical properties found in diffusion data.
 This talk reports our work on using the proposed $\beta$-VAE on simulated data from stochastic processes such as Brownian motion, fractional Brownian motion, and scaled Brownian motion. Our interpretation of the $\beta$-VAE reveals its ability to
 (1) help identify relevant parameters of the data, 
 (2) generate new trajectories with the learned statistics to facilitate the characterization, and 
 (3) benchmark machine learning architectures on datasets with known statistical properties.
   Our perspectives for future work include applying these techniques to other stochastic processes such as continuous time random walks and Ornstein-Uhlenbeck processes, as well as experimental applications for enhanced statistics, and benchmarking other architectures such as transformers." Write your response with exactly 3 paragraphs, separated with the markdown divider: ***.
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