5 papers
Duality Theory for Non-Markovian Linear Gaussian Models
Aditya Kudre, Heng-Sheng Chang, Prashant G. Mehta
This work develops a duality theory for partially observed linear Gaussian models in discrete time. The state process evolves according to a causal but non-Markovian (or higher-ord…
Differentiable Filtering for Learning Hidden Markov Models
Reginald Zhiyan Chen, Heng-Sheng Chang, Prashant G. Mehta
Hidden Markov Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Classical methods like the Baum-Welch…
What can we learn from signals and systems in a transformer? Insights for probabilistic modeling and inference architecture
Heng-Sheng Chang, Prashant G. Mehta
In the 1940s, Wiener introduced a linear predictor, where the future prediction is computed by linearly combining the past data. A transformer generalizes this idea: it is a nonlin…
Dual Filter: A Transformer-like Inference Architecture for Hidden Markov Models
Heng-Sheng Chang, Prashant G. Mehta
This paper presents a mathematical framework for causal nonlinear prediction in settings where observations are generated from an underlying hidden Markov model (HMM). Both the pro…
A Neural Network-based Framework for Fast and Smooth Posture Reconstruction of a Soft Continuum Arm
Tixian Wang, Heng-Sheng Chang, Seung Hyun Kim +9
A neural network-based framework is developed and experimentally demonstrated for the problem of estimating the shape of a soft continuum arm (SCA) from noisy measurements of the p…