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UID:10000309-1734652800-1734739199@www.neuropac.info
SUMMARY:ONM Student Talk: Ram Gaurav @ Virginia Tech
DESCRIPTION:Ramashish Gaurav (Ram) is a 3rd year Ph.D. student at Virginia Tech\, USA. He is supervised by Prof. Yang (Cindy) Yi in her BRICC Lab\, ECE @ VT. Of late\, Ram has been working on reservoir-based spiking models for Time Series Classification (TSC). Reservoir Computing is a well-established domain for time-series processing where a reservoir of statically (and recurrently) connected neurons compute high-dimensional temporal features\, over which a linear readout layer learns the mapping to the output. \nIn his recent work [1]\, Ram designed the Legendre-SNN (LSNN)\, a simple – yet high performing SNN model (for univariate TSC) where he has used the Legendre Delay Network (LDN) [2] as a non-spiking reservoir (in fact\, the LDN in LSNN is implemented with just basic matrix-operations). In a subsequent work (currently under review)\, he extended his LSNN to DeepLSNN that accounts for multivariate time-series signals too; upon experimenting with it\, he found that DeepLSNN models outperform a popular (and complex) LSTM-Conv integrated model [3] on more than 30% of 101 TSC datasets. His latest work is on the evaluation of Legendre-SNN on the Loihi-2 chip [4] — on which this talk is focused at. \nTalk details here\, time TBA.
URL:https://www.neuropac.info/event/onm-student-talk-ram-gaurav-virginia-tech/
LOCATION:Online
CATEGORIES:Talk
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