CausalBatch: solving complexity/performance tradeoffs for deep convolutional and LSTM networks for wearable activity recognition. - Lloyd Pellatt; Daniel Roggen (UbiComp/ISWC Adjunct, 2020)

Detecting Freezing of Gait with Earables Trained from VR Motion Capture Data. - Nobuyuki Oishi; Benedetta Heimler; Lloyd Pellatt; Meir Plotnik; Daniel Roggen (ISWC, 2021)

Fast Deep Neural Architecture Search for Wearable Activity Recognition by Early Prediction of Converged Performance. - Lloyd Pellatt; Daniel Roggen (ISWC, 2021)

A Concurrent Training Method of Deep-Learning Autoencoders in a Multi-user Interference Channel. - Lloyd Pellatt; Maziar M. Nekovee; Dehao Wu (ISWCS, 2021)

Slow Feature Preprocessing in Deep Neural Networks for Wearable Sensor-Based Locomotion Recognition. - Lukas Günthermann; Lloyd Pellatt; Daniel Roggen (PerCom Workshops, 2022)

A Public Repository to Improve Replicability and Collaboration in Deep Learning for HAR. - Lloyd Pellatt; Marius Bock; Daniel Roggen; Kristof Van Laerhoven (PerCom Workshops, 2022)

Speeding up deep neural architecture search for wearable activity recognition with early prediction of converged performance. - Lloyd Pellatt; Daniel Roggen (Frontiers Comput. Sci., 2022)

Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning. - Lloyd Pellatt; Fotios Drakopoulos; Shievanie Sabesan; Nicholas A. Lesica (CoRR, 2025)