Probabilistic Attention-to-Influence Neural Models for Event Sequences

Xiao Shou, D. Bhattacharjya, Tian Gao, D. Subramanian, Oktie Hassanzadeh +1 more
2/12/2026

No confident code implementation found for this paper.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Region-Aware Sequence-to-Sequence Learning for Hyperspectral Denoising

99% match1 repo22026

Denoising Autoregressive Transformers for Scalable Text-to-Image Generation

98% match2026

Harnessing Text-to-Image Diffusion Models for Category-Agnostic Pose Estimation

98% match2026

Online Pre-Training for Offline-to-Online Reinforcement Learning

98% match1 repo92026

Actor-Critic Alignment for Offline-to-Online Reinforcement Learning

98% match1 repo132026

Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning

98% match2026

Cite this paper

@article{shou2026probabilistic,
  title  = {Probabilistic Attention-to-Influence Neural Models for Event Sequences},
  author = {Xiao Shou and D. Bhattacharjya and Tian Gao and D. Subramanian and Oktie Hassanzadeh and Kristin P. Bennett},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:12386298945d5ebe80f1cb1ceb6d8f9e6ffcc85f},
  journal = {ICML 2023 2023}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/12386298945d5ebe80f1cb1ceb6d8f9e6ffcc85f.svg)](https://codeofpaper.com/paper/12386298945d5ebe80f1cb1ceb6d8f9e6ffcc85f)

Discussion