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Found 14 papers, 6 papers with code
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
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting conditions.
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.
