Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method effectively interpolates time-series data, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability.

Linking latent features with their semantic meanings can improve the interpretability of recommender systems [16]. In recent years, many recommender systems have Typically, topic modeling techniques are utilized to extract utilized textual data for topic extraction to enhance topics from textual data and align extracted topics to latent interpretability. However, our findings reveal a noticeable features. For example, [13] introduces an approach that combines deficiency in the coherence of keywords the traditional latent factor model with Latent Dirichlet within topics, resulting in low explainability of the Allocation (LDA) [3] to uncover topics correlated with the model. This paper introduces a novel approach latent factors of both products and users. On the other hand, called entropy regularization to address the issue, [12] adopts a convolutional neural network (CNN) to encode leading to more interpretable topics extracted from textual reviews into item embeddings. The convolutional kernels recommender systems, while ensuring that the performance are then utilized to extract topics that correspond to the of the primary task stays competitively latent factors of items.