Training contemporary AI models requires investment in procuring learnin...
We introduce SABRE, a novel framework for robust variational Bayesian
pe...
The Secure and Trustworthy Computing (SaTC) program within the National
...
Due to the immutable and decentralized nature of Ethereum (ETH) platform...
Video compression plays a significant role in IoT devices for the effici...
Deep neural networks use skip connections to improve training convergenc...
Steganography and digital watermarking are the tasks of hiding recoverab...
Text classification has become widely used in various natural language
p...
Deep Neural Networks (DNNs) have been shown to be susceptible to Trojan
...
The ubiquity and pervasiveness of modern Internet of Things (IoT) device...
Privacy-preserving federated learning allows multiple users to jointly t...
Deep neural network based face recognition models have been shown to be
...
A growing body of work has shown that deep neural networks are susceptib...
This paper proposes AdaTest, a novel adaptive test pattern generation
fr...
With the surge of Machine Learning (ML), An emerging amount of intellige...
Deepfakes and manipulated media are becoming a prominent threat due to t...
Video compression plays a crucial role in enabling video streaming and
c...
The goal of federated learning (FL) is to train one global model by
aggr...
Electrohydrodynamic-jet (e-jet) printing technique enables the
high-reso...
We propose HASHTAG, the first framework that enables high-accuracy detec...
Deep neural networks have been shown to be vulnerable to backdoor, or tr...
Splitting network computations between the edge device and a server enab...
Ethereum smart contracts are automated decentralized applications on the...
There has been a recent surge in adversarial attacks on deep learning ba...
With the abundance of large-scale deep learning models, it has become
po...
An emerging amount of intelligent applications have been developed with ...
Voice cloning is the task of learning to synthesize the voice of an unse...
Convolutional Neural Networks (CNNs) have made significant progress on
s...
We propose CLEANN, the first end-to-end framework that enables online
mi...
In this work, we provide an industry research view for approaching the
d...
In the contemporary big data realm, Deep Neural Networks (DNNs) are evol...
Authentication and identification methods based on human fingerprints ar...
Autoregressive convolutional neural networks (CNNs) have been widely
exp...
Federated learning (FL) is a machine learning setting where many clients...
Deep Neural Networks for image classification have been found to be
vuln...
Recent research has shown that Deep Neural Networks (DNNs) for image
cla...
This paper introduces ASCAI, a novel adaptive sampling methodology that ...
Reverse engineering of binary executables is a critical problem in the
c...
We propose a decentralized learning algorithm over a general social netw...
In this work, we demonstrate the existence of universal adversarial audi...
Training large and highly accurate deep learning (DL) models is
computat...
Deep Neural Networks have created a paradigm shift in our ability to
com...
Advancements in deep learning enable cloud servers to provide
inference-...
We present ARM2GC, a novel secure computation framework based on Yao's
G...
We consider the problem of training a machine learning model over a netw...
This paper proposes CodeX, an end-to-end framework that facilitates enco...
DNNs shall be considered as the intellectual property (IP) of the model
...
Adversarial Reprogramming has demonstrated success in utilizing pre-trai...
Deep neural networks (DNN) have demonstrated effectiveness for various
a...
The success of deep learning models is heavily tied to the use of massiv...