221 citations · 470 across the 37 of their papers we have counts for
5 papers · 1 filter
DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Wenrui Xu, Anas Enanaa, Keshab K. Parhi
Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications such as electroencephalogram (E…
LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks
Nanda K. Unnikrishnan, Keshab K. Parhi
In our prior work, LayerPipe, we had introduced an approach to accelerate training of convolutional, fully connected, and spiking neural networks by overlapping forward and backwar…
Robust Clustering using Hyperdimensional Computing
Lulu Ge, Keshab K. Parhi
This paper addresses the clustering of data in the hyperdimensional computing (HDC) domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has been pro…
Tensor Decomposition for Model Reduction in Neural Networks: A Review
Xingyi Liu, Keshab K. Parhi
Modern neural networks have revolutionized the fields of computer vision (CV) and Natural Language Processing (NLP). They are widely used for solving complex CV tasks and NLP tasks…
Classification using Hyperdimensional Computing: A Review
Lulu Ge, Keshab K. Parhi
Hyperdimensional (HD) computing is built upon its unique data type referred to as hypervectors. The dimension of these hypervectors is typically in the range of tens of thousands.…