arXiv Machine Learning
Aug 12

ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models

arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.

By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino
arXiv Machine Learning
Sep 3

A Unified Particle Filter LSTM for Data-Driven Process Simulation

The paper introduces a Unified Particle Filter LSTM (Unified PF‑LSTM) for data‑driven process simulation, which maintains a weighted set of recurrent‑state hypotheses to better capture latent process conditions from incomplete event logs. By summarizing this particle belief with a weighted mean and moment‑generating‑function features, the model predicts next‑activity probabilities and conditional sojourn‑time quantiles. Experiments on three real‑world emergency department datasets show that the framework consistently outperforms existing data‑driven baselines in reproducing routing, duration, and system‑level behavior, especially when process dynamics are only partially reflected in the logs.

By Parvin Malekzadeh, Opher Baron, Dmitry Krass