Machine learning publications

We intend to replace parts or all of a target pipeline with deep neural networks to achieve benefits such as increased accuracy or reduced computational requirement.

Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience.

We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation.

The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations. Ternary Connect (TC) \cite{lin2015neural} addresses this issue by restricting the…

We introduce and tackle the problem of zero-shot object detection, which aims to detect object classes which are not observed during training.

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can…

Abstract While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks.…

In this paper, we present a unique approach using lower precision weights for more efficient and faster training phase.

We present a system for learning descriptions of objects, and for subsequently recognizing learned objects, that functions in outdoor, natural environments.