Federated Learning (FL) is a distributed learning paradigm that empowers...
As deep learning continues to advance and is applied to increasingly com...
The significant success of Deep Neural Networks (DNNs) is highly promote...
Deep Learning (DL) models have achieved superior performance. Meanwhile,...
With the fast development of deep neural networks (DNNs), many real-worl...
Deep Learning (DL) models have achieved superior performance in many
app...
Deep Learning Recommendation Models (DLRM) are widespread, account for a...
This paper gives an overview of our ongoing work on the design space
exp...
Previous works proved that the combination of the linear neuron network ...
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the
incr...
Convolutional Neural Networks (CNNs) achieved great cognitive performanc...
Current state-of-the-art object detectors can have significant performan...
Recently, adversarial attacks can be applied to the physical world, caus...
Convolutional Neural Networks (CNNs) are used for a wide range of
image-...
The state-of-art DNN structures involve intensive computation and high m...
Alternating Direction Method of Multipliers (ADMM) has been used success...
Recently, Convolutional Neural Networks (CNNs) demonstrate a considerabl...
Recently, adversarial deception becomes one of the most considerable thr...
Based on filter magnitude ranking (e.g. L1 norm), conventional filter pr...
Neural network compression and acceleration are widely demanded currentl...
Deep neural networks (DNNs) although achieving human-level performance i...
The sophisticated structure of Convolutional Neural Network (CNN) allows...
One popular hypothesis of neural network generalization is that the flat...
Nowadays, machine learning based Automatic Speech Recognition (ASR) tech...
In recent years, neural networks have demonstrated outstanding effective...
With the excellent accuracy and feasibility, the Neural Networks have be...