Search Results for author:
Found 21 papers, 8 papers with code
An ACO-MPC Framework for Energy-Efficient and Collision-Free Path Planning in Autonomous Maritime Navigation
This paper proposes an integrated planner for automated vehicles (AVs) on ramps, utilizing an unsatisfactory level metric for efficiency and arrow-cluster-based sampling for safety.
Data-Driven Evolutionary Game-Based Model Predictive Control for Hybrid Renewable Energy Dispatch in Autonomous Ships
In this paper, we propose a data-driven Evolutionary Game-Based Model Predictive Control (EG-MPC) framework for the energy dispatch of a hybrid renewable energy system powering an autonomous ship.
Adaptive Field Effect Planner for Safe Interactive Autonomous Driving on Curved Roads
Autonomous driving has garnered significant attention for its potential to improve safety, traffic efficiency, and user convenience.
Intelligent road crack detection and analysis based on improved YOLOv8
As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life.
Industrial Internet Robot Collaboration System and Edge Computing Optimization
To address this issue in the framework of the Industrial Internet Robot Collaboration System, this paper proposes a global path control scheme for mobile robots based on deep learning.
Irrational Complex Rotations Empower Low-bit Optimizers
In this paper, we propose a novel optimizer state compression algorithm, namely $\pi$-Quant, which leverages the properties of irrational numbers (e. g., $\pi$) for memory-efficient training.
Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey
However, ensuring safety and efficiency in interactive during within dynamic and diverse environments is still a primary barrier to large-scale AV adoption.
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation
Federated Unlearning (FU) enables clients to selectively remove the influence of specific data from a trained federated learning model, addressing privacy concerns and regulatory requirements.
A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts
This paper introduces a learning-based algorithm tailored to foster safe and efficient driving behaviors across varying levels of traffic flows in roundabouts.
CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning
The key challenge of cross-modal domain-incremental learning (DIL) is to enable the learning model to continuously learn from novel data with different feature distributions under the same task without forgetting old ones.
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations
Our core idea is the construction and application of the class contribution momentum indicator from individual, relative, and holistic perspectives, thereby achieving an effective and efficient contribution evaluation of heterogeneous participants without relying on an auxiliary test dataset.
Exploring Context Window of Large Language Models via Decomposed Positional Vectors
Based on our findings, we design two training-free context window extension methods, positional vector replacement and attention window extension.
EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention
The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence.
Sequence-level Semantic Representation Fusion for Recommender Systems
The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts.
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
To address the above issue, we propose the Universal Feature Interaction Network (UFIN) approach for CTR prediction.
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference.
Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory
We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0. 94+/-0. 83 mm.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
