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Found 35 papers, 9 papers with code
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems
MIML has been proven effective in classifying white blood cells and tumor cells, with potential for broader application due to its inherent flexibility and transfer learning capability.
AVARS -- Alleviating Unexpected Urban Road Traffic Congestion using UAVs
In this paper, we argue that Unmanned Aerial Vehicles (UAVs) can play a crucial role in dealing with unexpected traffic congestion because UAVs with onboard cameras can be economically deployed when and where unexpected congestion occurs.
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System
To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities
Approaches evaluated include the Adversarial Co-training Network (ACN) and a combination of mmGAN and DeepMedic.
Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks
Traditional control and monitoring of water quality in drinking water distribution networks (WDN) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known.
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
Debiasing Neural Retrieval via In-batch Balancing Regularization
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
Control-Theoretic Modeling of Multi-Species Water Quality Dynamics in Drinking Water Networks: Survey, Methods, and Test Cases
Chlorine is a widely used disinfectant and proxy for water quality (WQ) monitoring in water distribution networks (WDN).
A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions
With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies.
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning
Therefore, our CoTV can well balance the achievement of the reduction of travel time, fuel, and emissions.
Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations
Computational fluid dynamics (CFD) simulations are broadly applied in engineering and physics.
Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges
When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced.
Decision Making
Explainable Artificial Intelligence (XAI)
+2
Fine-Grained Control of Artistic Styles in Image Generation
The style vectors are fed to the generator and discriminator to achieve fine-grained control.
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks.
Can CAV Reduce Non-Recurrent Urban Road Congestion?
A well-designed resilient and sustainable urban transportation system can recover quickly from the non-recurrent road congestion (NRC), which is often caused by en-route events (e. g., road closure due to car collisions).
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and error, which is a Markov decision process.
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
We use Elliptical Gaussian distributions to describe items and sequences with uncertainty.
Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting
As compared with previous or existing anti-counterfeiting mechanisms for banknotes, our method has a distinctive advantage: it ensures that even in the extreme case when counterfeiters have procured the same printing equipment and ink as used by a legitimate government, counterfeiting banknotes remains infeasible because of the difficulty to replicate a stochastic manufacturing process.
Cryptography and Security
Model Order Reduction for Water Quality Dynamics
Such representation is a byproduct of space- and time-discretization of the PDE modeling transport dynamics.
Transferable Graph Optimizers for ML Compilers
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code.
Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation
Nevertheless, there are still great challenges with brain midline delineation, such as the largely deformed midline caused by the mass effect and the possible morphological failure that the predicted midline is not a connected curve.
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.
Chip Placement with Deep Reinforcement Learning
To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas.
BusTime: Which is the Right Prediction Model for My Bus Arrival Time?
With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions.
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.
Heterogeneous Graph Matching Networks
Information systems have widely been the target of malware attacks.
Adversarial Defense Framework for Graph Neural Network
How to address the vulnerabilities and defense GNN against the adversarial attacks?
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process.
Kernelized Support Tensor Machines
In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.
Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.
