Rate-splitting multiple access (RSMA) uplink requires optimization of
de...
Vision-language models (VLMs) have shown impressive performance in
subst...
Generating visually grounded image captions with specific linguistic sty...
Current captioning approaches tend to generate correct but "generic"
des...
In this work, we systematically investigate linear multi-step methods fo...
The use of Physics-informed neural networks (PINNs) has shown promise in...
Mode collapse is still a major unsolved problem in generative adversaria...
A journal's impact and similarity with rivals is closely related to its
...
Fractional diffusion equations have been an effective tool for modeling
...
In recent years, vision and language pre-training (VLP) models have adva...
Background: Code summarization automatically generates the corresponding...
Data imputation is an effective way to handle missing data, which is com...
GSM-R is predicted to be obsoleted by 2030, and a suitable successor is
...
Accurate sensor calibration is a prerequisite for multi-sensor perceptio...
Recently, deep learning-based methods have reached an excellent performa...
With the exponential increase of the number of devices in the communicat...
In this paper, we propose a model-operator-data network (MOD-Net) for so...
Deep neural network (DNN) usually learns the target function from low to...
Previous feature alignment methods in Unsupervised domain adaptation(UDA...
Channel pruning is broadly recognized as an effective approach to obtain...
Why heavily parameterized neural networks (NNs) do not overfit the data ...
A supervised learning problem is to find a function in a hypothesis func...
Recent works show an intriguing phenomenon of Frequency Principle
(F-Pri...
The feedback capacities of the Gaussian multiple-access channel (GMAC) a...
How neural network behaves during the training over different choices of...
Graph neural networks (GNNs) extends the functionality of traditional ne...
We study the problem of distilling knowledge from a large deep teacher
n...
The interpretability of Convolutional Neural Networks (CNNs) is an impor...
Over the past decades, kinetic description of granular materials has rec...
In recent years, deep learning has shown impressive performance on many
...
The alternating direction method of multipliers (ADMM) has recently been...
Deep Graph Neural Networks (GNNs) are instrumental in graph classificati...
To achieve high coverage of target boxes, a normal strategy of conventio...
Recently, scene text recognition methods based on deep learning have spr...
Training deep models for lane detection is challenging due to the very s...
Graph Neural Networks (GNNs) have become a topic of intense research rec...
Along with fruitful applications of Deep Neural Networks (DNNs) to reali...
Non-orthogonal multiple access (NOMA) is a promising radio access techno...
It remains a puzzle that why deep neural networks (DNNs), with more
para...
How different initializations and loss functions affect the learning of ...
Convolution operations designed for graph-structured data usually utiliz...
3D face reconstruction from a single 2D image is a challenging problem w...
Since sparse unmixing has emerged as a promising approach to hyperspectr...
Recently, methods based on deep learning have dominated the field of tex...
We study the training process of Deep Neural Networks (DNNs) from the Fo...
Spatio-temporal information is very important to capture the discriminat...
A practical face recognition system demands not only high recognition
pe...
Face anti-spoofing (a.k.a presentation attack detection) has drawn growi...
Reinforcement learning agents need exploratory behaviors to escape from ...
The training of many existing end-to-end steering angle prediction model...