no code implementations • 16 Apr 2024 • Kechun Liu, Wenjun Wu, Joann G. Elmore, Linda G. Shapiro
Accurate cancer diagnosis remains a critical challenge in digital pathology, largely due to the gigapixel size and complex spatial relationships present in whole slide images.
no code implementations • 4 Apr 2024 • Haoran Li, Haolin Shi, Wenli Zhang, Wenjun Wu, Yong Liao, Lin Wang, Lik-Hang Lee, Pengyuan Zhou
Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors.
no code implementations • 12 Jan 2024 • Jiaxin Wang, Lingling Zhang, Jun Liu, Tianlin Guo, Wenjun Wu
The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances.
no code implementations • 26 Dec 2023 • Kun Lan, Haoran Li, Haolin Shi, Wenjun Wu, Yong Liao, Lin Wang, Pengyuan Zhou
Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration.
no code implementations • 15 Dec 2023 • Tianhao Peng, Wenjun Wu, Haitao Yuan, Zhifeng Bao, Zhao Pengrui, Xin Yu, Xuetao Lin, Yu Liang, Yanjun Pu
To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs.
Ranked #2 on Node Classification on Cornell
1 code implementation • 30 Jul 2023 • Tianhao Peng, Yu Liang, Wenjun Wu, Jian Ren, Zhao Pengrui, Yanjun Pu
Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students.
no code implementations • 30 Jul 2023 • Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Jie Luo, Wenjun Wu
In addition, the proposed framework is model-agnostic and can be applied to most of the current MARL algorithms.
no code implementations • 4 Apr 2023 • Da Li, Bo Tang, Xuyang Wang, Wenjun Wu, Lei Xue
Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements.
no code implementations • 28 Feb 2023 • Wenjun Wu, Bo Tang, Xuyang Wang
We investigate the constant-modulus (CM) waveform design for dual-function radar communication systems in the presence of clutter. To minimize the interference power and enhance the target acquisition performance, we use the signal-to-interference-plus-noise-ratio as the design metric. In addition, to ensure the quality of the service for each communication user, we enforce a constraint on the synthesis error of every communication signals. An iterative algorithm, which is based on cyclic optimization, Dinkinbach's transform, and alternating direction of method of multipliers, is proposed to tackle the encountered non-convex optimization problem. Simulations illustrate that the CM waveforms synthesized by the proposed algorithm allow to suppress the clutter efficiently and control the synthesis error of communication signals to a low level.
1 code implementation • 7 Feb 2023 • Simin Li, Jun Guo, Jingqiao Xiu, Pu Feng, Xin Yu, Aishan Liu, Wenjun Wu, Xianglong Liu
To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
1 code implementation • 30 Mar 2022 • Jiabin Lou, Rong Ding, Wenjun Wu
Concretely, the most commonly used Evolutionary Algorithms are decomposed into a series of operators, which constitute the operator library of the system.
no code implementations • 24 Jan 2021 • Jun Guo, Wei Bao, Jiakai Wang, Yuqing Ma, Xinghai Gao, Gang Xiao, Aishan Liu, Jian Dong, Xianglong Liu, Wenjun Wu
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
no code implementations • 22 Apr 2018 • Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh
To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis.