Deep learning

K. T. Mai, T. Davies, L. D. Griffin, E. Benetos, "Explaining the decisions of anomalous sound detectors", in 7th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), Nov. 2022.

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks. Lim, D-Y. & Sabanis, S. 2021. arXiv:2105.13937 [cs.LG]

Nicole Peinelt, Dong Nguyen, Maria Liakata (2020): tBERT - Topic Models and BERT Joining Forces for Semantic Similarity Detection. Proceedings of the 58th Conference of the Association for Computational Linguistics (ACL 2020), pages 7047-7055.

Gupta, Prateek, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, and Yoshua Bengio. "Hybrid models for learning to branch" NeurIPS 2020.

Bengio, Yoshua, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller et al. "Predicting Infectiousness for Proactive Contact Tracing." ICLR 2020.

Camuto, A., Willetts, M., Paige, B., Holmes, C., Roberts, S., (2020) Learning Bijective Feature Maps for Linear ICA

Willetts, M., Camuto, A., Rainforth, T., Roberts, S., Holmes, C., (2021) Improving VAEs’ Robustness to Adversarial Attack. International Conference of Learning Representations (ICLR)

Willetts, M., Roberts, S., Holmes, C. (2019) Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders. NeurIPS Bayesian Deep Learning Workshop 2019

J. T. Meech and P. Stanley-Marbell, "Efficient Programmable Random Variate Generation Accelerator from Sensor Noise," in IEEE Embedded Systems Letters, doi: 10.1109/LES.2020.3007005