TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials
<|reference_start|>TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials: We introduce a neural network named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), belonging to the TENNs (Temporal Neural Networks) architecture. We focus on interfacing these networks with event-based data to perform online spatiotemporal classification and detection with low latency. By virtue of using structured temporal kernels and event-based data, we have the freedom to vary the sample rate of the data along with the discretization step-size of the network without additional finetuning. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs. We achieved: 1) 99.59% accuracy with 192K parameters on the DVS128 hand gesture recognition dataset and 100% with a small additional output filter; 2) 99.58% test accuracy with 277K parameters on the AIS 2024 eye tracking challenge; and 3) 0.556 mAP with 576k parameters on the PROPHESEE 1 Megapixel Automotive Detection Dataset.<|reference_end|>
@article{pei2024tenns-pleiades:,
    title={TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials},
    author={Yan Ru Pei, Olivier Coenen},
    journal={arXiv preprint arXiv:2405.12179},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12179},
    primaryClass={cs.LG cs.AI}
}