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Found 9 papers, 1 papers with code
REFRAME: Reflective Surface Real-Time Rendering for Mobile Devices
This work tackles the challenging task of achieving real-time novel view synthesis on various scenes, including highly reflective objects and unbounded outdoor scenes.
A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets
Decision trees are essential yet NP-complete to train, prompting the widespread use of heuristic methods such as CART, which suffers from sub-optimal performance due to its greedy nature.
A Global Optimization Algorithm for K-Center Clustering of One Billion Samples
This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance.
Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention.
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task.
Boundary-aware Context Neural Network for Medical Image Segmentation
In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information.
Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks
While graph neural networks (GNNs) have shown a great potential in various tasks on graph, the lack of transparency has hindered understanding how GNNs arrived at its predictions.
Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation
In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation.
HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs
Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while maintaining the prediction accuracy deserves attention.
