Sparse Coding Lab

Research

We are exploring AI frameworks that mimic how the mammalian brain senses and understands the world. Our goal is to develop an AI system will learn much like an infant learns, by simply observing the world and learning through observation. Eventually, the model should learn the structure of the world and existing associations, and accurately make predictions. We are using neuromorphic software and hardware concepts such as sparse coding, top-down feedback, spiking neural networks, and neuronal dynamics to create a machine intelligence that has a better understanding of the world in which we live.

Recent Publications

E.Kim, J.Yarnall, P.Shah, G.Kenyon, "A Neuromorphic Sparse Coding Defense to Adversarial Images", International Conference on Neuromorphic Systems, ICONS, 2019.

Y.Watkins, A.Thresher, P.Schultz, A.Wild, A.Sornborger, E.Kim, G.Kenyon, "Towards Self-Organizing Neuromorphic Processors: Unsupervised Dictionary Learning via a Spiking Locally Competitive Algorithm", International Conference on Neuromorphic Systems, ICONS, 2019.

J.Springer, C.Strauss, A.Thresher, E.Kim, G.Kenyon, "Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples", arXiv:1811.07211, 2018.

E.Kim, K.McCoy, "Multimodal Deep Learning using Images and Text for Information Graphic Classification", ACM SIGACCESS Conference on Computers and Accessibility, Assets, 2018 (Best Paper Nominee).

E.Kim, D.Hannan, G.Kenyon, "Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons", International Conference on Computer Vision and Pattern Recognition, CVPR, 2018.

Recent Presentations and Posters

J.Yarnall, P.Shah, E.Kim, "A Neuromorphic Sparse Coding Defense to Adversarial Images", Sigma Xi Student Research Poster Symposium, Villanova, 2019

E.Kim, E.Lawson, K.Sullivan, G.Kenyon, "Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding", Neuro-inspired Computational Elements Workshop, NICE, 2019

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