SPICEs: Survey Papers as Interactive Cheat-sheet Embeddings

Prabhu, V.1, McAteer, M.2, Teehan, R.3 Rethinking ML Papers – ICLR 2021 Workshop Papers are hard to write. Survey papers are just that much harder. From the authors’ perspective, challenges include the responsibility to not erase out important work being done by (sometimes) adversarially aligned research groups, finding the right semantic clustering to sub-categorize individual contributions, […]

Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation

Manjunath, S.1, Nathaniel, A.2, Druce, J.1, German, S.1 arXiv:2008.05381v1 (August 2020) Recent advances in machine learning (ML) and computer vision tools have enabled applications in a wide variety of arenas such as financial analytics, medical diagnostics, and even within the Department of Defense. However, their widespread implementation in real-world use cases poses several challenges: (1) […]

Privacy Preserving Neural Network Inference on Encrypted Data with GPUs

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Takabi, H.1, Podschwadt, R.2, Druce, J.3, Wu, C.3, and Procopio, K3. Presented at Privacy in Machine Learning, NeurIPS 2019 Workshop, Vancover, BC (December 2019) Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered. MLaaS is essentially a set of services that provides machine […]

Application of the DeepSense Deep Learning Framework to Determination of Activity Context from Smartphone Data

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Bracken, B., Manjunath, S., German, S., Monnier, C., and Farry, M. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Volume 63, Issue 1, Pages 792-796 (November 2019) Current methods of assessing health are infrequent, costly, and require advanced medical equipment. 92% of US adults carry mobile phones, and 77% carry smartphones with advanced sensors (Smith, 2017). […]

Machine Learning-Enabled Adaptation of Information Fusion Software Systems

Fry, G.1, Samawi, T.1, Lu, K.1, Pfeffer, A.1, Wu, C.1, Marotta, S.1, Reposa, M.1, Chong, S.2 2019 22th International Conference on Information Fusion (FUSION) Real-time control systems must fuse information from multiple sensors to perform mission tasks in dynamic environments. The volatility of these environments can cause sensor degradation or failure, reducing the accuracy and […]

Predicting Signatures of Future Malware Variants

Howard, M., Pfeffer, A., Dalal, M., and Reposa, M. The 12th International Conference on Malicious and Unwanted Software (MALWARE 2017) One of the challenges of malware defense is that the attacker has the advantage over the defender. In many cases, an attack is successful and causes damage before the defender can even begin to prepare […]

Long-Range Forecasting Using COMPASS Machine Learning

O’Connor, A.1, Bell, R.2, Kirtman, B.2, and Gorman, J.1 Presented at the 7th International Workshop of Climate Informatics hosted by the National Center for Atmospheric Research, Boulder, CO (September 2017) The Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS) uses machine learning to create long-range forecasts of the probability that future conditions will differ from […]

Operational Planning using Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

O’Connor1, A., Kirtman2, B., Harrison1, S., and Gorman1, J. Presented at SPIE Defense + Commercial Sensing, Baltimore Convention Center, Baltimore, MD (April 2016) The US Navy faces several limitations when planning operations in regard to forecasting environmental conditions. Currently, mission analysis and planning tools rely heavily on short-term (less than a week) forecasts or long-term statistical […]