Papers Edge Classification
“Edge Classification” 태그가 달린 논문 56편 · 필터 해제
Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a…
CPUData CompressionDeep Reinforcement LearningEdge Classification+1Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Edges in many real-world social/information networks are associated with rich text information (e.g., user-user communications or user-product reviews). However, mainstream network representation learning models focus on…
Edge ClassificationLink PredictionRepresentation LearningGRANDE: a neural model over directed multigraphs with application to anti-money laundering
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling transaction networks using graph neural …
Edge ClassificationGraph Representation LearningManagementRepresentation LearningVisuotactile Affordances for Cloth Manipulation with Local Control
Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visua…
Edge ClassificationPose EstimationEvince the artifacts of Spoof Speech by blending Vocal Tract and Voice Source Features
With the rapid advancement in synthetic speech generation technologies, great interest in differentiating spoof speech from the natural speech is emerging in the research community. The identification of these synthetic …
Edge ClassificationA Framework for Large Scale Synthetic Graph Dataset Generation
Recently there has been increasing interest in developing and deploying deep graph learning algorithms for many tasks, such as fraud detection and recommender systems. Albeit, there is a limited number of publicly availa…
BenchmarkingDataset GenerationDrug DiscoveryEdge Classification+5Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First,…
Edge ClassificationLink PredictionDiGress: Discrete Denoising diffusion for graph generation
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noi…
DenoisingEdge ClassificationGraph GenerationPolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?
Most (3D) multi-object tracking methods rely on appearance-based cues for data association. By contrast, we investigate how far we can get by only encoding geometric relationships between objects in 3D space as cues for …
3D Multi-Object TrackingEdge ClassificationGraph Neural NetworkMulti-Object Tracking+1Efficient and effective training of language and graph neural network models
Can we combine heterogenous graph structure with text to learn high-quality semantic and behavioural representations? Graph neural networks (GNN)s encode numerical node attributes and graph structure to achieve impressiv…
Edge ClassificationGraph Neural NetworkLanguage ModelingLanguage Modelling+1Revisiting the role of heterophily in graph representation learning: An edge classification perspective
Graph representation learning aim at integrating node contents with graph structure to learn nodes/graph representations. Nevertheless, it is found that many existing graph learning methods do not work well on data with …
Edge ClassificationGraph LearningGraph Representation LearningRepresentation LearningGraph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly …
Edge ClassificationGraph EmbeddingGraph Representation LearningNode Classification+1Graph Neural Network for Cell Tracking in Microscopy Videos
We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its node…
3D Multi-Object TrackingCell TrackingEdge ClassificationGraph Neural Network+1Artificial Intelligence based tool wear and defect prediction for special purpose milling machinery using low-cost acceleration sensor retrofits
Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostl…
Edge ClassificationNeighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks
In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph ar…
ClassificationEdge ClassificationGraph LearningGraph Neural Network+4HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approac…
Edge ClassificationExtracting Buildings In Remote Sensing ImagesGraph ReconstructionGRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of…
Edge ClassificationGraph Representation LearningLink PredictionNode Classification+1Classifying Dyads for Militarized Conflict Analysis
Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) …
ArticlesEdge ClassificationSTaCK: Sentence Ordering with Temporal Commonsense Knowledge
Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological se…
Edge ClassificationSentenceSentence OrderingAdaptive Edge Attention for Graph Matching with Outliers
Graph matching aims at establishing correspondence between node sets of given graphs while keeping the consistency between their edge sets. However, outliers in practical scenarios and equivalent learning of edge represe…
Edge ClassificationGraph Matching