| Fast deep neural architecture search for wearable activity recognition by early prediction of converged performance L Pellatt, D Roggen Proceedings of the 2021 ACM International Symposium on Wearable Computers, 1-6, 2021 | 6 | 2021 |
| Mapping vicon motion tracking to 6-Axis IMU data for wearable activity recognition L Pellatt, A Dewar, A Philippides, D Roggen Activity and Behavior Computing, 3-20, 2021 | 6 | 2021 |
| Detecting freezing of gait with earables trained from VR motion capture data N Oishi, B Heimler, L Pellatt, M Plotnik, D Roggen Proceedings of the 2021 ACM International Symposium on Wearable Computers, 33-37, 2021 | 5 | 2021 |
| CausalBatch: solving complexity/performance tradeoffs for deep convolutional and LSTM networks for wearable activity recognition L Pellatt, D Roggen Adjunct Proceedings of the 2020 ACM International Joint Conference on …, 2020 | 5 | 2020 |
| Slow feature preprocessing in deep neural networks for wearable sensor-based locomotion recognition L Günthermann, L Pellatt, D Roggen 2022 IEEE International Conference on Pervasive Computing and Communications …, 2022 | 4 | 2022 |
| Speeding up deep neural architecture search for wearable activity recognition with early prediction of converged performance L Pellatt, D Roggen Frontiers in Computer Science 4, 914330, 2022 | 2 | 2022 |
| A Public Repository to Improve Replicability and Collaboration in Deep Learning for HAR L Pellatt, M Bock, D Roggen, K Van Laerhoven 2022 IEEE International Conference on Pervasive Computing and Communications …, 2022 | 1 | 2022 |
| Compute-optimised deep learning for wearable time series with fast multi-objective architecture search L Pellatt University of Sussex, 2024 | | 2024 |
| A Concurrent Training Method of Deep-Learning Autoencoders in a Multi-user Interference Channel L Pellatt, M Nekovee, D Wu 2021 17th International Symposium on Wireless Communication Systems (ISWCS), 1-6, 2021 | | 2021 |
