A set of variables is the Markov blanket of a random variable if it cont...
The electronic design automation of analog circuits has been a longstand...
We consider solving partial differential equations (PDEs) with Fourier n...
Quantum Monte Carlo coupled with neural network wavefunctions has shown
...
With the continuous development and change exhibited by large language m...
Notifications are important for the user experience in mobile apps and c...
This paper presents a non-Hermitian physics-inspired voltage-controlled
...
Neural network-based models have found wide use in automatic long-term
e...
Tsetlin machine (TM) is a logic-based machine learning approach with the...
In this paper, we consider a cooperative communication network where mul...
Deep learning-based personalized recommendation systems are widely used ...
Visual anomaly detection, an important problem in computer vision, is us...
Video understanding is an important problem in computer vision. Currentl...
Deep learning methods have been shown to be effective in representing
gr...
Harnessing parity-time (PT) symmetry with balanced gain and loss profile...
The design automation of analog circuits is a longstanding challenge. Th...
Studying the history of music may provide a glimpse into the development...
The ubiquitous presence of printed circuit boards (PCBs) in modern elect...
Personalized recommendation is an important class of deep-learning
appli...
Discovery program (DISC) is an affirmative action policy used by the New...
The design automation of analog circuits is a longstanding challenge in ...
In today's digital world, interaction with online platforms is ubiquitou...
Processing-in-memory (PIM) architectures have demonstrated great potenti...
In a modern power system, real-time data on power generation/consumption...
Price discrimination, which refers to the strategy of setting different
...
E-commerce voice ordering systems need to recognize multiple product nam...
The Tsetlin Machine (TM) is a novel machine-learning algorithm based on
...
Medical image segmentation plays an essential role in developing
compute...
With the deployment of smart sensors and advancements in communication
t...
We study text representation methods using deep models. Current methods,...
Many emerging cyber-physical systems, such as autonomous vehicles and ro...
Learning disentangled representations leads to interpretable models and
...
The Tsetlin Machine (TM) is a novel machine learning algorithm with seve...
Birkhoff's representation theorem (Birkhoff, 1937) defines a bijection
b...
There is considerable evidence that deep neural networks are vulnerable ...
The Tsetlin Machine (TM) is a recent machine learning algorithm with sev...
There is an inherent problem in the way students are evaluated - be it
s...
Adapting machine translation systems in the real world is a difficult
pr...
Due to a lack of medical resources or oral health awareness, oral diseas...
Due to a lack of medical resources or oral health awareness, oral diseas...
Personalized recommendation systems leverage deep learning models and ac...
We propose a novel quaternion product unit (QPU) to represent data on 3D...
Recent works propose neural network- (NN-) inspired analog-to-digital
co...
Recent advances in machine learning, especially techniques such as deep
...
Star glyphs are a well-researched visualization technique to represent
m...
The widespread application of deep learning has changed the landscape of...
We introduce a curriculum learning approach to adapt generic neural mach...
In this paper, we apply a new promising tool for pattern classification,...
Recent advances in machine learning, especially techniques such as deep
...
Machine translation systems based on deep neural networks are expensive ...