Hyperspectral image change detection (HSI-CD) aims to identify the
diffe...
Testing Autonomous Driving Systems (ADSs) is a critical task for ensurin...
Noise suppression (NS) models have been widely applied to enhance speech...
Incremental few-shot semantic segmentation (IFSS) aims to incrementally
...
This paper addresses the computation of normalized solid angle measure o...
Talking head generation is to generate video based on a given source ide...
As deep learning is pervasive in modern applications, many deep learning...
Visual Question Answering (VQA) becomes one of the most active research
...
In this paper,
we study the episodic reinforcement learning (RL) probl...
We study an assortment optimization problem under a multi-purchase choic...
In blockchain systems, the design of transaction fee mechanisms is essen...
Reinforcement Learning (RL) techniques have drawn great attention in man...
In addition to maximizing the total revenue, decision-makers in lots of
...
A major challenge for ridesharing platforms is to guarantee profit and
f...
Sea fog significantly threatens the safety of maritime activities. This ...
Learning to perform tasks by leveraging a dataset of expert observations...
We present the first backdoor attack against the lane detection systems ...
Classifying SPECT images requires a preprocessing step which normalizes ...
Many practical applications of reinforcement learning require agents to ...
Price discrimination, which refers to the strategy of setting different
...
Self-attention architectures have emerged as a recent advancement for
im...
We study the optimal batch-regret tradeoff for batch linear contextual
b...
Automated segmentation in medical image analysis is a challenging task t...
Despite the success that metric learning based approaches have achieved ...
The control variates (CV) method is widely used in policy gradient estim...
We study deep reinforcement learning (RL) algorithms with delayed reward...
In this paper, we propose a new challenging task named as partial
multi-...
With the successful adoption of machine learning on electronic health re...
Quality-Diversity (QD) is a concept from Neuroevolution with some intrig...
Long-term temporal credit assignment is an important challenge in deep
r...
Optical aerial images change detection is an important task in earth
obs...
Probabilistic software analysis aims at quantifying the probability of a...
AI planning can be cast as inference in probabilistic models, and
probab...
We tackle the problem of conditioning probabilistic programs on distribu...
We study MNL bandits, which is a variant of the traditional multi-armed
...
Reinforcement learning from self-play has recently reported many success...
In this paper, we continue our work on Video-Query based Video Moment
re...
Understanding how certain brain regions relate to a specific neurologica...
In this paper, we focus on Video Query based Video Moment Retrieval (VQ-...
We study multinomial logit bandit with limited adaptivity, where the
alg...
Motivated by practical needs such as large-scale learning, we study the
...
In this paper we consider the problem of learning an ϵ-optimal
policy fo...
Spatial details and context correlations are two types of critical
infor...
We study a variant of the thresholding bandit problem (TBP) in the conte...
Autonomous driving vehicles (ADVs) are implemented with rich software
fu...
We study the reinforcement learning problem in the setting of finite-hor...
We consider the following problem in this paper: given a set of n
distri...
Due to the broad attack surface and the lack of runtime protection, pote...
Probabilistic programs with mixed support (both continuous and discrete
...
The choice of activation function in deep networks has a significant eff...