The last decade has witnessed the success of deep learning and the surge...
It is well known that the numerical solution of the Non-Fickian flows at...
Co-movement pattern mining from GPS trajectories has been an intriguing
...
When to solve math problems, most language models take a sampling strate...
We present and analyze a new hybridizable discontinuous Galerkin method ...
OD matrix estimation is a critical problem in the transportation domain....
Policy gradient algorithms are an important family of deep reinforcement...
Optical flow estimation has made great progress, but usually suffers fro...
Given a basic block of instructions, finding a schedule that requires th...
In distributed transaction processing, atomic commit protocol (ACP) is u...
Data-free quantization aims to achieve model quantization without access...
As a promising solution for model compression, knowledge distillation (K...
Many actor-critic deep reinforcement learning (DRL) algorithms have achi...
Verifiable ledger databases protect data history against malicious tampe...
The proposal of Pseudo-Lidar representation has significantly narrowed t...
In the Metaverse, the physical space and the virtual space co-exist, and...
Machine learning-based methods have achieved successful applications in
...
This paper analyzes a class of globally divergence-free (and therefore
p...
In [ESAIM: M2AN, 54(2020), 2229-2264], we proposed an HDG method to
appr...
This paper proposes a perception-shared and swarm trajectory global opti...
Dynamic occupancy maps were proposed in recent years to model the obstac...
Partial label learning (PLL) is an important problem that allows each
tr...
Obstacle avoidance of quadrotors in dynamic environments is still a very...
Human beings keep exploring the physical space using information means. ...
The idea of using the recurrent neural network for visual attention has
...
In [SIAM J. Numer. Anal., 59 (2), 720-745], we proved quasi-optimal
L^∞ ...
Deep learning models usually require a large amount of labeled data to
a...
Relative localization is a prerequisite for the cooperation of aerial sw...
Deep learning has achieved great success in a wide spectrum of multimedi...
Bus timetable optimization is a key issue to reduce operational cost of ...
Relational databases are the de facto standard for storing and querying
...
Most, if not all, modern deep learning systems restrict themselves to a
...
The randomized or cross-validated split of training and testing sets has...
A Cartesian decomposition of a coherent configuration X is defined as
a ...
Fatigue detection is valued for people to keep mental health and prevent...
Alphas are stock prediction models capturing trading signals in a stock
...
Recently, distributed controller architectures have been quickly gaining...
This paper proposes semi-discrete and fully discrete hybridizable
discon...
The SIMT execution model is commonly used for general GPU development. C...
Although modern machine learning, in particular deep learning, has achie...
Federated Learning (FL) is a promising distributed learning paradigm, wh...
BERT-enhanced neural machine translation (NMT) aims at leveraging
BERT-e...
With the ever-increasing adoption of machine learning for data analytics...
This paper presents a novel vision-based obstacle avoidance system for f...
In J. Sci. Comput., 81: 2188-2212, 2019, we considered a superconvergent...
Federated learning (FL) is an emerging paradigm that enables multiple
or...
Planimation is a modular and extensible open source framework to visuali...
Q-learning with value function approximation may have the poor performan...
We prove quasi-optimal L^∞ norm error estimates (up to logarithmic
facto...
Data collaboration activities typically require systematic or protocol-b...