Following the success of GPT4, there has been a surge in interest in
mul...
Chlorophyll concentration can well reflect the nutritional status and al...
Over decades, neuroscience has accumulated a wealth of research results ...
Recent work on Neural Radiance Fields (NeRF) has demonstrated significan...
The explosive growth of computation and energy cost of artificial
intell...
Today's commodity camera systems rely on compound optics to map light
or...
Object detection is a critical component of various security-sensitive
a...
This work presents ReSync, a Riemannian subgradient-based algorithm for
...
As learning-based methods make their way from perception systems to
plan...
The subgradient method is one of the most fundamental algorithmic scheme...
We develop a method for hybrid analyses that uses external controls to
a...
3D shape completion from point clouds is a challenging task, especially ...
Talking head generation is to generate video based on a given source ide...
Byzantine quorum systems provide higher throughput than proofof-work and...
In contrast to proof-of-work replication, Byzantine replicated systems
m...
The Multiplane Image (MPI), containing a set of fronto-parallel RGBA lay...
Joint channel estimation and signal detection (JCESD) is crucial in wire...
Indoor location-based services rely on the availability of sufficiently
...
Deep neural networks are vulnerable to adversarial attacks. We consider
...
Distributed stochastic optimization has drawn great attention recently d...
The symmetric Nonnegative Matrix Factorization (NMF), a special but impo...
With the ever-growing model size and the limited availability of labeled...
Adversarial attacks can easily fool object recognition systems based on ...
Numerical reasoning over hybrid data containing tables and long texts ha...
To achieve the goal of providing the best possible care to each patient,...
We present a new method for estimating the Neural Reflectance Field (NRe...
While cross entropy (CE) is the most commonly used loss to train deep ne...
We consider simultaneous predictive distributions for independent Poisso...
In this paper, we study a second-order accurate and linear numerical sch...
Machine learning poses severe privacy concerns as it is shown that the
l...
Blockchain is an emerging decentralized data collection, sharing and sto...
Nonsmooth optimization finds wide applications in many engineering field...
Decentralized Actor-Critic (AC) algorithms have been widely utilized for...
In this work, we provide a fundamental unified convergence theorem used ...
Recent machine reading comprehension datasets such as ReClor and LogiQA
...
It is well-known that the Allen-Cahn equation not only satisfies the ene...
The energy dissipation law and the maximum bound principle (MBP) are two...
When training deep neural networks for classification tasks, an intrigui...
Contact tracing has been proven an effective approach to mitigate the vi...
In this paper, we consider the distributed optimization problem where n
...
Error propagation is a general but crucial problem in online semi-superv...
Seismic data fault detection has recently been regarded as a 3D image
se...
We study the random reshuffling (RR) method for smooth nonconvex optimiz...
We aim for domestic robots to operate indoor for long-term service. Unde...
In this paper, we introduce a set-theoretic approach for mobile robot
lo...
It is well known that the classic Allen-Cahn equation satisfies the maxi...
A core strength of knockoff methods is their virtually limitless
customi...
Recently, DETR and Deformable DETR have been proposed to eliminate the n...
Detection faults in seismic data is a crucial step for seismic structura...
We provide the first global optimization landscape analysis of
Neural Co...