The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

In this paper, we study the problem of temporal video grounding (TVG), which aims to predict
the starting/ending time points of moments described by a text sentence within a long …

Deep convolutional neural networks (CNNs) are widely used for image classification. Deep
CNNs often require a large memory and abundant computation resources, limiting their …

Global channel pruning (GCP) aims to remove a subset of channels (filters) across different
layers from a deep model without hurting the performance. Previous works focus on either …

Almost all rotating machinery in the industry has bearings as their key building block and
most of these machines run 24× 7. This makes bearing health prediction an active research …

Despite the superior performance, it is challenging to deploy foundation models or large
language models (LLMs) due to their massive parameters and computations. While pruning …

While deep neural networks have achieved superior performance in a variety of intelligent
applications, the increasing computational complexity makes them difficult to be deployed …

Neural network (NN) compression via techniques such as pruning quantization requires
setting compression hyperparameters (eg number of channels to be pruned bitwidths for …

As neural networks grow in size and complexity, inference speeds decline. To combat this,
one of the most effective compression techniques–channel pruning–removes channels from …

Incorporating equivariance to symmetry groups as a constraint during neural network
training can improve performance and generalization for tasks exhibiting those symmetries …