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Found 23 papers, 6 papers with code
On Complexity of Stability Analysis in Higher-order Ecological Networks through Tensor Decompositions
Complex ecological networks are often characterized by intricate interactions that extend beyond pairwise relationships.
Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation
In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation.
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data
Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction.
Hypergraph Analysis Toolbox for Chromosome Conformation
Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions.
Gradient-based Bi-level Optimization for Deep Learning: A Survey
In this survey, we first give a formal definition of the gradient-based bi-level optimization.
A Survey on Hyperlink Prediction
As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes.
Unbiased Implicit Feedback via Bi-level Optimization
Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure.
Structure-aware Protein Self-supervised Learning
Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.
Generalized DataWeighting via Class-Level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Generalized Data Weighting via Class-level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design
Summary of Contribution: We propose an iterative prediction-and-optimization algorithm for multilevel distribution network design for e-logistics and evaluate its operational value for online retailers.
Hypergraph Dissimilarity Measures
In this paper, we propose two novel approaches for hypergraph comparison.
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning.
RoIFusion: 3D Object Detection from LiDAR and Vision
When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e. g. camera, LIDAR) typically increases the robustness of 3D detectors.
Controllability of Hypergraphs
In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory.
Tensor Entropy for Uniform Hypergraphs
In this paper, we develop the notion of entropy for uniform hypergraphs via tensor theory.
Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
Unlike conventional operation that directly applies MLPs on high-dimensional features of point cloud, our model goes wider by splitting features into groups in advance, and each group with certain smaller depth is only responsible for respective MLP operation, which can reduce complexity and allows to encode more useful information.
Fast Hierarchical Neural Network for Feature Learning on Point Cloud
In order to balance model performance and complexity, we introduce a novel neural network architecture exploiting local features from a manually subsampled point set.
GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
In this paper, we propose a novel neural network for point cloud, dubbed GAPNet, to learn local geometric representations by embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP) layers.
Focus Manipulation Detection via Photometric Histogram Analysis
With the rise of misinformation spread via social media channels, enabled by the increasing automation and realism of image manipulation tools, image forensics is an increasingly relevant problem.
Image Splicing Detection via Camera Response Function Analysis
Recent advances on image manipulation techniques have made image forgery detection increasingly more challenging.
Depth Recovery From Light Field Using Focal Stack Symmetry
The other is a data consistency measure based on analysis-by-synthesis, i. e., the difference between the synthesized focal stack given the hypothesized depth map and that from the LF.
Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras
Our bilateral consistency metric is used to indicate the probability of occlusions by analyzing the SCams.
