Transformers for graph data are increasingly widely studied and successf...
Recommender systems now consume large-scale data and play a significant ...
Learning a categorical distribution comes with its own set of challenges...
Contrastive learning has emerged as a premier method for learning
repres...
Offline model-based optimization aims to maximize a black-box objective
...
The mobile communication enabled by cellular networks is the one of the ...
To offer accurate and diverse recommendation services, recent methods us...
The challenge in learning from dynamic graphs for predictive tasks lies ...
The machine learning community has mainly relied on real data to benchma...
There have been several recent efforts towards developing representation...
In offline model-based optimization, we strive to maximize a black-box
o...
There has been an increased interest in applying machine learning techni...
Implicit feedback is frequently used for developing personalized
recomme...
Multiple Instance Learning (MIL) is a weakly supervised learning problem...
Dual-energy computed tomography (DECT) is an advanced CT scanning techni...
Presently with technology node scaling, an accurate prediction model at ...
Spatio-temporal forecasting has numerous applications in analyzing wirel...
Reasoning in a temporal knowledge graph (TKG) is a critical task for
inf...
Personalized recommender systems are increasingly important as more cont...
Personalized recommender systems are playing an increasingly important r...
Recently there has been a significant effort to automate UV mapping, the...
Given the convenience of collecting information through online services,...
Adversarial attacks can affect the performance of existing deep learning...
Node classification in attributed graphs is an important task in multipl...
Graphs are ubiquitous in modelling relational structures. Recent endeavo...
Personalized recommendation is ubiquitous, playing an important role in ...
The chronological order of user-item interactions can reveal time-evolvi...
Graph convolutional neural networks (GCNN) have numerous applications in...
Graph convolutional neural networks (GCNN) have been successfully applie...
We address the task of identifying densely connected subsets of multivar...
Recently, techniques for applying convolutional neural networks to
graph...
Sequential state estimation in non-linear and non-Gaussian state spaces ...
The multi-agent swarm system is a robust paradigm which can drive effici...
Microwave-based breast cancer detection has been proposed as a complemen...
In this paper, we consider a generalized multivariate regression problem...
We consider the problem of multivariate regression in a setting where th...
This paper describes a graph clustering algorithm that aims to minimize ...
This paper presents greedy gossip with eavesdropping (GGE), a novel
rand...