Quantile regression (QR) is a statistical tool for distribution-free
est...
We study the problem of routing and scheduling of real-time flows over a...
Vision-Transformers are widely used in various vision tasks. Meanwhile, ...
Hyperparameter tuning is a common technique for improving the performanc...
Deep neural networks are known to be susceptible to adversarial perturba...
Quantile regression (QR) is a powerful tool for estimating one or more
c...
A central challenge in building robotic prostheses is the creation of a
...
Understating and controlling generative models' latent space is a comple...
The success of learning with noisy labels (LNL) methods relies heavily o...
Energy-saving LIDAR camera for short distances estimates an object's dis...
Mechanical image stabilization using actuated gimbals enables capturing
...
Unsupervised learning has always been appealing to machine learning
rese...
The process of fertilizing a human egg outside the body in order to help...
Convolutional Neural Networks (CNNs) have become common in many fields
i...
Even though deep learning have shown unmatched performance on various ta...
Deep neural networks are known to be vulnerable to inputs with malicious...
Neural network quantization enables the deployment of large models on
re...
Convolutional neural networks (CNNs) have become the dominant neural net...
We consider the problem of localizing relevant subsets of non-rigid geom...
Learning from one or few visual examples is one of the key capabilities ...
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy i...
Recently, deep learning has become a de facto standard in machine learni...
Example synthesis is one of the leading methods to tackle the problem of...
Beauty is in the eye of the beholder. This maxim, emphasizing the
subjec...
Convolutional Neural Networks (CNN) has become more popular choice for
v...
We consider the tasks of representing, analyzing and manipulating maps
b...
Cardiac ultrasound imaging requires a high frame rate in order to captur...
Frame rate is a crucial consideration in cardiac ultrasound imaging and ...
We propose a fully-convolutional neural-network architecture for image
d...
Learning to classify new categories based on just one or a few examples ...
Distance metric learning (DML) has been successfully applied to object
c...
We present a novel method for training a neural network amenable to infe...
We present a novel method for training deep neural network amenable to
i...
We present DeepISP, a full end-to-end deep neural model of the camera im...
The cost-effectiveness and practical harmlessness of ultrasound imaging ...
Multidimensional Scaling (MDS) is one of the most popular methods for
di...
The increasing demand for high image quality in mobile devices brings fo...
Poisson distribution is used for modeling noise in photon-limited imagin...
Solving inverse problems with iterative algorithms is popular, especiall...
Three important properties of a classification machinery are: (i) the sy...
In this work we study the properties of deep neural networks (DNN) with
...
In recent years, a lot of attention has been devoted to efficient neares...
Maximally stable component detection is a very popular method for featur...