Published in Under review IJCAI 2025, 2025
We propose a mixture of expert model which has subnetworks dedicated to detection animal species from specific geographical locations. This work builds towards a single species detector that prunes parts of the network not needed for a camera trap site without finetuning the model.
Recommended citation: Forthcoming. Forthcoming
Published in Under review ICML 2025, 2024
We explore per patch conditional computation for mobile vision transformers (vision only case), that will eventually be used for single-tower multimodal edge models.
Recommended citation: Mensah, Emmanuel Azuh, et al. "Towards Vision Mixture of Experts for Wildlife Monitoring on the Edge." arXiv preprint arXiv:2411.07834 (2024). https://arxiv.org/html/2411.07834v1
Published in Computing Within Limits, 2021
In the face of ecological and political limits, we propose a computational approach to opening borders to migrants in an automated way while ensuring safety for the host nation.
Recommended citation: Mensah, E. A., Singanamalla, S., Anderson, R., & Heimerl, K. (2021). When Borders Blur-Overcoming Political Limits with Computing in Truly Global Societies. https://computingwithinlimits.org/2021/papers/limits21-mensah.pdf
Published in Interspeech, 2019
This work presented a multimodal approach to learn bilingual lexicon directly from speech signals in two languages without the need for text by using vision as an interlingua. The approach starts a line of inquiry that can build word level translation between a pair of languages using say youtube videos that have similar objects but with speech in the two languages.
Recommended citation: Azuh, Emmanuel, David Harwath, and James R. Glass. "Towards Bilingual Lexicon Discovery From Visually Grounded Speech Audio." INTERSPEECH. 2019. http://groups.csail.mit.edu/sls/publications/2019/EmmanuelAzuh_Interspeech-2019.PDF
Published in IEEE Sensors, 2018
We use a deep autoencoder approach to learn representations of multivariate physiological signals that can be hashed and used to compute similarities between patients to assist in predicting critical events.
Recommended citation: Dhamala, J., Azuh, E., Al-Dujaili, A., Rubin, J., & O’Reilly, U. M. (2018). Multivariate time-series similarity assessment via unsupervised representation learning and stratified locality sensitive hashing: Application to early acute hypotensive episode detection. IEEE Sensors Letters, 3(1), 1-4. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8506445