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Found 18 papers, 7 papers with code
DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting
Accurate traffic forecasting is essential for effective urban planning and congestion management.
EAN-MapNet: Efficient Vectorized HD Map Construction with Anchor Neighborhoods
Therefore, we propose EAN-MapNet for Efficiently constructing HD map using Anchor Neighborhoods.
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells
Moreover, a novel frequency domain denoising network, named FDNet, is proposed for astrocyte segmentation.
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models.
FairCompass: Operationalising Fairness in Machine Learning
As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions.
DANAA: Towards transferable attacks with double adversarial neuron attribution
While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments.
Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction.
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss.
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
Resilient Output Containment Control of Heterogeneous Multiagent Systems Against Composite Attacks: A Digital Twin Approach
Third, according to the reconstructed leader dynamics, we design decentralized solvers that calculate the output regulator equations on CPL.
A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources.
DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL
Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC).
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.
Efficient Pressure: Improving efficiency for signalized intersections
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.
Crop and weed classification based on AutoML
CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature.
A Machine Learning Framework for Data Ingestion in Document Images
Paper documents are widely used as an irreplaceable channel of information in many fields, especially in financial industry, fostering a great amount of demand for systems which can convert document images into structured data representations.
On the Strong Equivalences of LPMLN Programs
Firstly, we present the notions of p-strong and w-strong equivalences between LPMLN programs.
On the Strong Equivalences for LPMLN Programs
In this paper, we study the strong equivalence for LPMLN programs, which is an important tool for program rewriting and theoretical investigations in the field of logic programming.
Logic in Computer Science D.1.6
