Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling
approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors …

Network training error and sparsity are two critical factors in optimizing the model
parameters of existing neuro-evolution algorithms. Alleviating the curse of dimensionality in …

Deep learning Recommendation Models (DLRMs) plays an important role in various
application domains. However, existing DLRM training systems require a large number of …

We introduce TQCompressor, a novel method for neural network model compression with
improved tensor decompositions. We explore the challenges posed by the computational …

Symmetric Sparse Matrix-Vector Multiplication (SSpMV) is a prevalent operation in
numerous application domains (eg, physical simulations, machine learning, and graph …