Zobrazeno 1 - 10
of 467
pro vyhledávání: '"Liu, Guizhong"'
Autor:
Liu, Chenxing, Liu, Guizhong
While Centralized Training with Decentralized Execution (CTDE) has become the prevailing paradigm in Multi-Agent Reinforcement Learning (MARL), it may not be suitable for scenarios in which agents can fully communicate and share observations with eac
Externí odkaz:
http://arxiv.org/abs/2404.11831
Autor:
Cui, Fei, Fang, Jiaojiao, Wu, Xiaojiang, Lai, Zelong, Yang, Mengke, Jia, Menghan, Liu, Guizhong
Stochastic video prediction enables the consideration of uncertainty in future motion, thereby providing a better reflection of the dynamic nature of the environment. Stochastic video prediction methods based on image auto-regressive recurrent models
Externí odkaz:
http://arxiv.org/abs/2404.11576
In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furthermore, this weakness is magnified when the concerned graph is characteri
Externí odkaz:
http://arxiv.org/abs/2403.03465
Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (DPs), which leads to weak pred
Externí odkaz:
http://arxiv.org/abs/2402.18127
Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning relation-aware graph structure
Externí odkaz:
http://arxiv.org/abs/2307.01507
Goal-conditioned hierarchical reinforcement learning (GCHRL) decomposes long-horizon tasks into sub-tasks through a hierarchical framework and it has demonstrated promising results across a variety of domains. However, the high-level policy's action
Externí odkaz:
http://arxiv.org/abs/2306.17484
Many scenes in real life can be abstracted to the sparse reward visual scenes, where it is difficult for an agent to tackle the task under the condition of only accepting images and sparse rewards. We propose to decompose this problem into two sub-pr
Externí odkaz:
http://arxiv.org/abs/2205.09448
Publikováno v:
Data Mining and Knowledge Discovery (2022)
Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires reasoning over the m
Externí odkaz:
http://arxiv.org/abs/2110.12679
Autor:
Fang, Jiaojiao, Liu, Guizhong
Self-supervised deep learning-based 3D scene understanding methods can overcome the difficulty of acquiring the densely labeled ground-truth and have made a lot of advances. However, occlusions and moving objects are still some of the major limitatio
Externí odkaz:
http://arxiv.org/abs/2108.03893
Autor:
Fang, Jiaojiao, Liu, Guizhong
Self-supervised learning of depth and ego-motion from unlabeled monocular video has acquired promising results and drawn extensive attention. Most existing methods jointly train the depth and pose networks by photometric consistency of adjacent frame
Externí odkaz:
http://arxiv.org/abs/2108.01980