arXiv AI

DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining

arXiv:2606. 14283v1 Announce Type: cross Abstract: Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring.

arXiv AI

Aug 26

Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library

The paper introduces SPICE, a Python framework that reimplements three popular deep‑learning methods for Predictive Process Mining (PPM) using PyTorch. It provides a common, highly configurable base to enable reproducible and robust comparison of PPM models, addressing issues of reproducibility, transparency, and usability. The authors benchmark SPICE against the original reported metrics and fair metrics across 11 datasets.

By Oliver Stritzel, Nick H\"uhnerbein, Simon Rauch, Itzel Zarate, Lukas Fleischmann, Moike Buck, Attila Lischka, Christian Frey

benchmarks

arXiv AI

Aug 17

Breaking Chains with Trees: Model-Parallel Deep Learning with $\mathcal{O}(\log N)$ Time Complexity

arXiv:2606. 21497v2 Announce Type: replace-cross Abstract: Modern deep neural networks are trained using error backpropagation, which requires sequential forward and backward computations across network layers.

By Neeraj Mohan Sushma, Aditya Nagarsekar, Cabrel Teguemne Fokam, Robin Schiewer, Amit Kumar Pal, Anand Subramoney, David Kappel

arXiv AI

Jun 15

DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation

arXiv:2506. 14202v4 Announce Type: replace-cross Abstract: End-to-end backpropagation requires storing activations throughout all layers, creating memory bottlenecks that limit model scalability.

By Makoto Shing, Masanori Koyama, Takuya Akiba

llms diffusion

arXiv Machine Learning

Sep 1

Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection

arXiv:2604.09166v3 Announce Type: replace Abstract: Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotate...

By Jennifer Werner, Justus Arweiler, Indra Jungjohann, Jochen Schmid, Fabian Jirasek, Hans Hasse, Michael Bortz

efficiency

arXiv AI

Jul 21

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

arXiv:2607. 17783v1 Announce Type: cross Abstract: Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases.

By Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu, Johannes De Smedt

computer-vision

arXiv AI

6d ago

Neural Bridge Processes

Neural Bridge Processes (NBPs) replace the input‑independent forward kernel of Neural Diffusion Processes with an input‑anchored bridge trajectory, allowing conditioning inputs to influence the noisy training states. When input and output dimensions differ, NBPs learn an output‑space anchor that guides the generative path without altering the denoising backbone. Theoretical analysis shows that this anchoring yields pathwise input distinguishability, injects input information into noisy states, and provides a direct gradient pathway, leading to consistent performance gains across synthetic regression, EEG, CylinderFlow, and image regression tasks.

By Jian Xu, Yican Liu, Delu Zeng, John Paisley, Qibin Zhao

diffusion safety

arXiv AI

Jul 10

Deep Learning Method for Stationary Distribution of Reflected Brownian Motion

arXiv:2607. 08091v1 Announce Type: cross Abstract: The stationary distribution of reflected Brownian motion (RBM) plays an important role in the analysis of high-dimensional stochastic systems, yet closed-form solutions are known only for a few special cases.

By Jim Dai, Zhanhao Zhang

arXiv Machine Learning

Sep 15

On the Potential of Multi-Task Learning in Predictive Process Monitoring

arXiv:2609.13477v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution suppor...

By Lukas Kirchdorfer, Keyvan Amiri Elyasi, Heiner Stuckenschmidt

arXiv Machine Learning

Jun 16

David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

arXiv:2606. 15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering.

By Hans Weytjens, Ingo Weber

llms benchmarks

arXiv Machine Learning

Sep 1

Tracing distinguishability through transformer processing with stochastic LayerNorm

arXiv:2608.30720v1 Announce Type: new Abstract: Representational similarity is foundational to analyses of deep networks, yet distances between point-valued representations are not intrinsically tied...

By Kieran Murphy

llms fine-tuning efficiency safety

arXiv AI

Jun 15

Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches

arXiv:2303. 09209v2 Announce Type: replace Abstract: Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes.

By Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Riccardo Graziosi, Francesca Meneghello, Massimiliano Ronzani

reinforcement-learning benchmarks

arXiv AI

Jul 28

Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks

arXiv:2410. 02596v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found applications in various important tasks, attracting great research interest in their training algorithms.

By Rui Hu, Yifan Zhang, Zhuoran Li, Longbo Huang

benchmarks

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