# STRODE: Stochastic Boundary Ordinary Differential Equation

arXiv: https://arxiv.org/abs/2107.08273 · HTML: https://mlsanity.com/paper/2107.08273/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 17 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models](https://mlsanity.com/paper/1810.01367/) (2018)
- [Neural Jump Stochastic Differential Equations](https://mlsanity.com/paper/1905.10403/) (2019)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Simple Recurrent Units for Highly Parallelizable Recurrence](https://mlsanity.com/paper/1709.02755/) (2017)
- [Deep Graph Random Process for Relational-Thinking-Based Speech Recognition](https://mlsanity.com/paper/2007.02126/) (2020)
- [Recent Progresses in Deep Learning based Acoustic Models (Updated)](https://mlsanity.com/paper/1804.09298/) (2018)

## Built on by (most cited first, 4 of 4)

- [Advancing Test-Time Adaptation in Wild Acoustic Test Settings](https://mlsanity.com/paper/2310.09505/) (2023)
- [Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics](https://mlsanity.com/paper/2207.04166/) (2022)
- [Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset](https://mlsanity.com/paper/2402.05547/) (2024)
- [Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization](https://mlsanity.com/paper/2205.02670/) (2022)

## Abstract (arXiv)

Perception of time from sequentially acquired sensory inputs is rooted in everyday behaviors of individual organisms. Yet, most algorithms for time-series modeling fail to learn dynamics of random event timings directly from visual or audio inputs, requiring timing annotations during training that are usually unavailable for real-world applications. For instance, neuroscience perspectives on postdiction imply that there exist variable temporal ranges within which the incoming sensory inputs can affect the earlier perception, but such temporal ranges are mostly unannotated for real applications such as automatic speech recognition (ASR). In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training. STRODE allows the usage of differential equations to sample from the posterior point processes, efficiently and analytically. We further provide theoretical guarantees on the learning of STRODE. Our empirical results show that our approach successfully infers event timings of time series data. Our method achieves competitive or superior performances compared to existing state-of-the-art methods for both synthetic and real-world datasets.