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Found 22 papers, 10 papers with code
Fixed-relative-switch strategies for learning based event-triggered control of nonlinear multiagent systems
This paper investigates event-triggered control for consensus tracking in nonlinear semi-strict-feedback multi-agent systems (MASs) with unknown states and subject to disturbances.
Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications
Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomous driving in today's 5G wireless communication world, we present Navigation Secure (NavSecure).
Rethinking Structure Learning For Graph Neural Networks
Surprisingly, our empirical observations and theoretical analysis show that no matter which type of graph structure construction methods are used, after feeding the same GSL bases to the newly constructed graph, there is no MI gain compared to the original GSL bases.
SE(3)-bi-equivariant Transformers for Point Cloud Assembly
Given a pair of point clouds, the goal of assembly is to recover a rigid transformation that aligns one point cloud to the other.
Disentangling Heterogeneous Knowledge Concept Embedding for Cognitive Diagnosis on Untested Knowledge
Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students' proficiency on UKCs.
ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models
To enhance the capabilities of ConvLLaVA, we propose two critical optimizations.
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Hybrid Event-triggered Control of Nonlinear System with Full State Constraints and Disturbance
This article focuses on the problem of adaptive tracking control for a specific type of nonlinear system that is subject to full-state constraints via a hybrid event-triggered control (HETC) strategy.
Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL
Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias.
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL
Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities.
Procedural Fairness in Machine Learning
We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_{FAE}$, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of the ML models.
EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks
Spiking Neural Networks (SNNs), which operate on an event-driven paradigm through sparse spike communication, emerge as a natural fit for addressing this challenge.
Get the Ball Rolling: Alerting Autonomous Robots When to Help to Close the Healthcare Loop
To facilitate the advancement of research in healthcare robots without human intervention or commands, we introduce the Autonomous Helping Challenge, along with a crowd-sourcing large-scale dataset.
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
This method enhances the model's ability to generate medical captions and answer complex medical queries.
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
In the CPT and SFT phases, Qilin-Med achieved 38. 4% and 40. 0% accuracy on the CMExam test set, respectively.
Heterogeneous Generative Knowledge Distillation with Masked Image Modeling
Masked image modeling (MIM) methods achieve great success in various visual tasks but remain largely unexplored in knowledge distillation for heterogeneous deep models.
Neuromorphic Auditory Perception by Neural Spiketrum
Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems.
Constrained Reinforcement Learning for Dynamic Material Handling
Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.
USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots
In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots.
Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation
In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence.
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
By evaluating on challenging datasets including CIFAR-10, CIFAR- 100 and ImageNet, the proposed method demonstrates the state-of-the-art performance in terms of accuracy, latency and energy preservation.
An Efficient Multi-Indicator and Many-Objective Optimization Algorithm based on Two-Archive
We also analyzed how normalizing affected the indicator-based algorithm and observed that the normalized $I_{\epsilon+}$ indicator is better at finding extreme solutions and can reduce the influence of each objective's different extent of contribution to the indicator due to its different scope.
Theoretical understanding of adversarial reinforcement learning via mean-field optimal control
A new mean-field Pontryagin's maximum principle is proposed for reinforcement learning with implicit terminal constraints.
