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Found 54 papers, 23 papers with code
STW-MD: A Novel Spatio-Temporal Weighting and Multi-Step Decision Tree Method for Considering Spatial Heterogeneity in Brain Gene Expression Data
Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal.
XRMDN: A Recurrent Mixture Density Networks-based Architecture for Short-Term Probabilistic Demand Forecasting in Mobility-on-Demand Systems with High Volatility
This will lead to the forecasting result severely deviating from the true demand value due to the high volatility existing in demand.
Composite Quantile Factor Models
This paper introduces the method of composite quantile factor model for factor analysis in high-dimensional panel data.
Collaborative Graph Neural Networks for Attributed Network Embedding
Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.
Towards Personalized Cold-Start Recommendation with Prompts
Recommender systems play a crucial role in helping users discover information that aligns with their interests based on their past behaviors.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
DeepMerge: Deep Learning-Based Region-Merging for Image Segmentation
Accurate segmentation of large areas from very high spatial-resolution (VHR) remote sensing imagery remains a challenging issue in image analysis.
Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking
In this paper, we first time propose an on-street parking recommendation (OPR) task to directly recommend a parking space for a driver.
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs.
RCDT: Relational Remote Sensing Change Detection with Transformer
The proposed RCDT consists of three major components, a weight-sharing Siamese Backbone to obtain bi-temporal features, a Relational Cross Attention Module (RCAM) that implements offset cross attention to obtain bi-temporal relation-aware features, and a Features Constrain Module (FCM) to achieve the final refined predictions with high-resolution constraints.
Contrastive Knowledge Graph Error Detection
To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED).
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages.
Boosted p-Values for High-Dimensional Vector Autoregression
Assessing the statistical significance of parameter estimates is an important step in high-dimensional vector autoregression modeling.
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
To this end, we propose Randomized Sparse Computation, which for the first time demonstrate the potential of training GNNs with approximated operations.
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
Graph Contrastive Learning with Personalized Augmentation
Thus, blindly augmenting all graphs without considering their individual characteristics may undermine the performance of GCL arts. To deal with this, we propose the first principled framework, termed as \textit{G}raph contrastive learning with \textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL by allowing each graph to choose its own suitable augmentation operations. In essence, GPA infers tailored augmentation strategies for each graph based on its topology and node attributes via a learnable augmentation selector, which is a plug-and-play module and can be effectively trained with downstream GCL models end-to-end.
Generative Adversarial Framework for Cold-Start Item Recommendation
Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set.
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data.
Lassoed Boosting and Linear Prediction in Equities Market
We consider a two-stage estimation method for linear regression that uses the lasso in Tibshirani (1996) to screen variables and re-estimate the coefficients using the least-squares boosting method in Friedman (2001) on every set of selected variables.
Subtractive Aggregation for Attributed Network Anomaly Detection
It aims to detect nodes that significantly deviate from their corresponding background.
Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping
The integrated results combined both the characteristic of UAV and mobile mapping vehicle point cloud, confirming the practicability of the proposed joint data acquisition platform and the effectiveness of spatio-temporal-spectral-angular observation model.
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.
GLSD: The Global Large-Scale Ship Database and Baseline Evaluations
In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks.
Pan-sharpening via High-pass Modification Convolutional Neural Network
Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose a novel pan-sharpening convolutional neural network based on a high-pass modification block.
SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising
In this study, we propose a novel HSI denoising network, termed SSCAN, that combines group convolutions and attention modules.
Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use.
Monitoring urban ecosystem service value using dynamic multi-level grids
Ecosystem valuation is a method of assigning a monetary value to an ecosystem with its goods and services, often referred to as ecosystem service value (ESV).
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases
The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted.
A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds
Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks.
A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds
Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks.
Dynamic Memory based Attention Network for Sequential Recommendation
It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.
Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data
In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement.
Social and Information Networks
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
Uniqueness of Meromorphic Functions With Respect To Their Shifts Concerning Derivatives
An example in the article shows that the first derivative of $f(z)=\frac{2}{1-e^{-2z}}$ sharing $0$ CM and $1,\infty$ IM with its shift $\pi i$ cannot obtain they are equal.
Complex Variables 30D35
Local Composite Quantile Regression for Regression Discontinuity
We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs.
Simulating multi-exit evacuation using deep reinforcement learning
We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping.
A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming
To fill the gap, in this work, we propose an innovative data-driven dynamic stochastic programming (DD-DSP) framework for time-series decision-making problem, which involves three components: GRU, Gaussian Mixture Model (GMM) and SP.
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.
Teaching Machine Comprehension with Compositional Explanations
Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.
DDKSP: A Data-Driven Stochastic Programming Framework for Car-Sharing Relocation Problem
Car-sharing issue is a popular research field in sharing economy.
Translating multispectral imagery to nighttime imagery via conditional generative adversarial networks
The results of this study prove the possibility of multispectral-to-nighttime translation and further indicate that, with the additional social media data, the generated nighttime imagery can be very similar to the ground-truth imagery.
Multi-Channel Graph Convolutional Networks
To further improve the graph representation learning ability, hierarchical GNN has been explored.
Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling
Its performance is largely influenced by the annotation quality and quantity in supervised learning scenarios, and obtaining ground truth labels is often costly.
Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation
Named entity recognition (NER) identifies typed entity mentions in raw text.
Ranked #1 on
Named Entity Recognition
on NCBI-disease
A Two-Stage Stochastic Programming Model for Car-Sharing Problem using Kernel Density Estimation
Car-sharing problem is a popular research field in sharing economy.
Auto-GNN: Neural Architecture Search of Graph Neural Networks
First, the search space of GNN is different from the ones in existing NAS work.
Ranked #37 on
Node Classification
on Cora
A Multimodal Vision Sensor for Autonomous Driving
This paper describes a multimodal vision sensor that integrates three types of cameras, including a stereo camera, a polarization camera and a panoramic camera.
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority.
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result.
Multi-Label Adversarial Perturbations
Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies.
A General Multi-agent Epistemic Planner Based on Higher-order Belief Change
Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK.
