Large language models (LLMs) encode a vast amount of world knowledge acq...
Neural networks are known to be susceptible to adversarial samples: smal...
Important research efforts have focused on the design and training of ne...
Eliminating examination bias accurately is pivotal to apply click-throug...
Pre-trained language models have achieved great success in various
large...
Conventional clustering methods based on pairwise affinity usually suffe...
We develop the first end-to-end sample complexity of model-free policy
g...
Direct policy search has been widely applied in modern reinforcement lea...
Gradient-based methods have been widely used for system design and
optim...
Cross-dataset emotion recognition as an extremely challenging task in th...
Heavy Ball (HB) nowadays is one of the most popular momentum methods in
...
Energy is an essential, but often forgotten aspect in large-scale federa...
In this paper, we consider the policy evaluation problem in multi-agent
...
Inspired by the humans' cognitive ability to generalise knowledge and sk...
Value-based methods play a fundamental role in Markov decision processes...
In recent years, the incidence of depression is rising rapidly worldwide...
Many existing region-of-attraction (ROA) analysis tools find difficulty ...
Motivated by the recent empirical success of policy-based reinforcement
...
Medical image segmentation is one of the fundamental problems for artifi...
On the increase of major depressive disorders (MDD), many researchers pa...
The main challenges of image-to-image (I2I) translation are to make the
...
Floods are highly uncertain events, occurring in different regions, with...
Person re-identification via 3D skeletons is an emerging topic with grea...
Skeleton-based person re-identification (Re-ID) is an emerging open topi...
With the rapid growth of time-critical applications in smart grid, robot...
Humans have a strong class-agnostic object segmentation ability and can
...
When applying imitation learning techniques to fit a policy from expert
...
By definition, object detection requires a multi-task loss in order to s...
Sharding is the prevalent approach to breaking the trilemma of simultane...
Direct policy search serves as one of the workhorses in modern reinforce...
Recently, policy optimization for control purposes has received renewed
...
In this paper, we focus on unsupervised representation learning for
skel...
Person re-identification (Re-ID) via gait features within 3D skeleton
se...
Smart contract has been regarded as one of the most promising and appeal...
Gait-based person re-identification (Re-ID) is valuable for safety-criti...
Intelligence services are playing an increasingly important role in the
...
Action recognition via 3D skeleton data is an emerging important topic i...
Markovian jump linear systems (MJLS) are an important class of dynamical...
Human gait refers to a daily motion that represents not only mobility, b...
As we power through to the future, in-vehicle communications reliance on...
Background: Depression has become a major health burden worldwide, and
e...
Depression is a common mental disorder worldwide which causes a range of...
According to the World Health Organization, the number of mental disorde...
According to the World Health Organization, the number of mental disorde...
Recently, policy optimization for control purposes has received renewed
...
This paper is concerned with a numerical solution of the acoustic scatte...
Policy optimization (PO) is a key ingredient for reinforcement learning ...
Effective features can improve the performance of a model, which can thu...
In this paper, we provide a unified analysis of temporal difference lear...
Rapid and massive adoption of mobile/ online payment services has brough...