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Found 13 papers, 3 papers with code
Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images
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.
DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling
Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors.
Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery
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.
2D-Guided 3D Gaussian Segmentation
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.
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
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
CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning
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.
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning
In addition, the proposed framework is model-agnostic and can be applied to most of the current MARL algorithms.
Co-Design for Spectral Coexistence between RIS-aided MIMO Radar and MIMO Communication Systems
Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements.
Constant-Modulus Waveform Design for Dual-Function Radar-Communication Systems in the Presence of Clutter
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.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
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.
Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms
Concretely, the most commonly used Evolutionary Algorithms are decomposed into a series of operators, which constitute the operator library of the system.
A Comprehensive Evaluation Framework for Deep Model Robustness
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).
HeteroMed: Heterogeneous Information Network for Medical Diagnosis
To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis.
