1 citations · 1 across the 6 of their papers we have counts for
8 papers
Continuous-Time Machine Learning: A Unified Mathematical Perspective
Waleed Razzaq, Yun-Sheng Zhao, Yun-Bo Zhao
Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when observations are sampled at ar…
Neuronal Stochastic Attention Circuit (NSAC) for Probabilistic Representation Learning
Waleed Razzaq, Yun-Bo Zhao
Reliable uncertainty quantification in continuous-time (CT) representation learning remains nascent, particularly within CT attention literature. We introduce the Neuronal Stochast…
FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning
Waleed Razzaq, Yun-Bo Zhao
Continuous-time (CT) Transformers improve irregular and long-range modeling over CT-RNNs by exploiting inputs or outputs embeddings with continuous dynamics. However, the core scal…
A Novel Multimodal RUL Framework for Remaining Useful Life Estimation with Layer-wise Explanations
Waleed Razzaq, Yun-Bo Zhao
Estimating the Remaining Useful Life (RUL) of mechanical systems is pivotal in Prognostics and Health Management (PHM). Rolling-element bearings are among the most frequent causes…
Error-Centric PID Untrained Neural-Net (EC-PIDUNN) For Nonlinear Robotics Control
Waleed Razzaq
Classical Proportional-Integral-Derivative (PID) control has been widely successful across various industrial systems such as chemical processes, robotics, and power systems. Howev…
Neuronal Attention Circuit (NAC) for Representation Learning
Waleed Razzaq, Yun-Bo Zhao
Attention improves representation learning over RNNs, but its discrete nature limits continuous-time (CT) modeling. We introduce Neuronal Attention Circuit (NAC), a novel, biologic…
Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics
Waleed Razzaq, Yun-Bo Zhao
Development of reliable and physically interpretable probabilistic frameworks for industrial prognostics remain nascent, and existing literature is often insensitive as inputs move…
CARLE: A Hybrid Deep-Shallow Learning Framework for Robust and Explainable RUL Estimation of Rolling Element Bearings
Waleed Razzaq, Yun-Bo Zhao
Prognostic Health Management (PHM) systems monitor and predict equipment health. A key task is Remaining Useful Life (RUL) estimation, which predicts how long a component, such as…