8 papers
Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors
Tao Chen, Yexu Zhou, Zhi Gong +9
Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences. Recent Prior-Data F…
Explainable Deep Learning Framework for Human Activity Recognition
Yiran Huang, Yexu Zhou, Haibin Zhao +2
In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making proc…
Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable Factors
Yiran Huang, Haibin Zhao, Yexu Zhou +2
In recent years, deep learning has emerged as a potent tool across a multitude of domains, leading to a surge in research pertaining to its application in the wearable human activi…
MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollers
Tobias King, Yexu Zhou, Tobias Röddiger +1
Designing domain specific neural networks is a time-consuming, error-prone, and expensive task. Neural Architecture Search (NAS) exists to simplify domain-specific model developmen…
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning
Yiran Huang, Yexu Zhou, Till Riedel +2
Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering. De…
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning
Yiran Huang, Yexu Zhou, Michael Hefenbrock +3
The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficia…
McXai: Local model-agnostic explanation as two games
Yiran Huang, Nicole Schaal, Michael Hefenbrock +4
To this day, a variety of approaches for providing local interpretability of black-box machine learning models have been introduced. Unfortunately, all of these methods suffer from…
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
Yexu Zhou, Yuting Gao, Yiran Huang +3
An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method,…