no code implementations • 10 Dec 2024 • Xinyue Hu, Wei Ye, Jiaxiang Tang, Eman Ramadan, Zhi-Li Zhang
We propose a novel MDC video codec, NeuralMDC, demonstrating how bidirectional transformers trained for masked token prediction can vastly simplify the design of MDC video codec.
no code implementations • 16 Nov 2021 • Yongshuai Liu, Jiaxin Ding, Zhi-Li Zhang, Xin Liu
Network slicing is proposed as a promising solution for resource utilization in 5G and future networks to address this dire need.
no code implementations • 29 Sep 2021 • Xin Zhang, Yanhua Li, Ziming Zhang, Christopher Brinton, Zhenming Liu, Zhi-Li Zhang, Hui Lu, Zhihong Tian
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
no code implementations • NeurIPS 2020 • Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang
This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency?
1 code implementation • 2 Oct 2020 • Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang
This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency?
no code implementations • 12 Aug 2020 • Zhi-Li Zhang, Quanyan Zhu
This paper studies the deception applied on agent in a partially observable Markov decision process.
2 code implementations • 31 May 2020 • Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang
NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.
Ranked #1 on
Next-basket recommendation
on TaFeng
no code implementations • 31 Jan 2020 • Xinyue Hu, Haoji Hu, Saurabh Verma, Zhi-Li Zhang
Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems.
1 code implementation • 22 Sep 2019 • Saurabh Verma, Zhi-Li Zhang
By learning task-independent graph embeddings across diverse datasets, DUGNN also reaps the benefits of transfer learning.
Ranked #3 on
Graph Classification
on COLLAB
(using extra training data)
no code implementations • 3 May 2019 • Saurabh Verma, Zhi-Li Zhang
In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting.
1 code implementation • 21 May 2018 • Saurabh Verma, Zhi-Li Zhang
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision.
Ranked #29 on
Graph Classification
on NCI1
2 code implementations • NeurIPS 2017 • Saurabh Verma, Zhi-Li Zhang
For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation.
no code implementations • 21 Nov 2011 • Yanhua Li, Wei Chen, Yajun Wang, Zhi-Li Zhang
Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing.
Social and Information Networks Discrete Mathematics Physics and Society E.1; H.3.3