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Found 8 papers, 3 papers with code
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
In this study, we take one step toward this new research area by exploring a feasible strategy to fully exploit VFM features for RGB-thermal scene parsing.
Ranked #1 on
Thermal Image Segmentation
on KP day-night
Trace and Edit Relation Associations in GPT
This study introduces a novel approach for analyzing and modifying entity relationships in GPT models, diverging from ROME's entity-focused methods.
On the Initialization of Graph Neural Networks
In this paper, we analyze the variance of forward and backward propagation across GNN layers and show that the variance instability of GNN initializations comes from the combined effect of the activation function, hidden dimension, graph structure and message passing.
RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing
Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10, 407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes.
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI.
COVRECON: Combining Genome-scale Metabolic Network Reconstruction and Data-driven Inverse Modeling to Reveal Changes in Metabolic Interaction Networks
Previously, we have derived a mathematical method addressing this problem using metabolomics data for the inverse calculation of a biochemical Jacobian network.
Network In Graph Neural Network
However, wider hidden layers can easily lead to overfitting, and incrementally adding more GNN layers can potentially result in over-smoothing. In this paper, we present a model-agnostic methodology, namely Network In Graph Neural Network (NGNN ), that allows arbitrary GNN models to increase their model capacity by making the model deeper.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
