Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Most state-of-the-art deep neural networks are overparameterized and exhibit a high
computational cost. A straightforward approach to this problem is to replace convolutional …

This paper proposes an improved training algorithm for binary neural networks in which both
weights and activations are binary numbers. A key but fairly overlooked feature of the current …

Learning from spatio-temporal data has numerous applications such as human-behavior
analysis, object tracking, video compression, and physics simulation. However, existing …

In this paper we propose two scale-inspired local feature extraction methods based on
Quantum Convolutional Neural Network (QCNN) in the Tensorflow quantum framework for …

Training large models from scratch usually costs a substantial amount of resources. Towards
this problem, recent studies such as bert2BERT and LiGO have reused small pretrained …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Federated learning is an emerging concept that trains the machine learning models with the
local distributed data sets, without sending the raw data to the data center. But, in the …

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 …

Yoga has become an integral part of human life to maintain a healthy body and mind in
recent times. With the growing, fast-paced life and work from home, it has become difficult for …