Search Results for author:
Found 24 papers, 6 papers with code
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data.
Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel
In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input.
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.
Local Contrastive Feature learning for Tabular Data
Contrastive self-supervised learning has been successfully used in many domains, such as images, texts, graphs, etc., to learn features without requiring label information.
Deep Forest with Hashing Screening and Window Screening
To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening.
OPP-Miner: Order-preserving sequential pattern mining
To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values.
GraSSNet: Graph Soft Sensing Neural Networks
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection.
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.
Predictive Modeling of Hospital Readmission: Challenges and Solutions
Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days, after the discharge.
Learning Graph Neural Networks with Positive and Unlabeled Nodes
By generating multiple graphs at different distance levels, based on the adjacency matrix, we develop a long-short distance attention model to model these graphs.
User Response Prediction in Online Advertising
What type of data are available for user response prediction?
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
We argue that tripartite networks are common in real world applications, and the essential challenge of the representation learning is the heterogeneous relations between various node types and links in the network.
Evolutionary Architecture Search for Graph Neural Networks
In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
Multi-Label Graph Convolutional Network Representation Learning
The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning.
Ranked #30 on
Multi-Label Classification
on MS-COCO
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task.
A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis
Text-to-image synthesis refers to computational methods which translate human written textual descriptions, in the form of keywords or sentences, into images with similar semantic meaning to the text.
Attributed Network Embedding via Subspace Discovery
In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.
Ranked #1 on
Node Clustering
on Facebook
Search Efficient Binary Network Embedding
In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network.
SINE: Scalable Incomplete Network Embedding
In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs.
Social and Information Networks
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes.
Social and Information Networks
Network Representation Learning: A Survey
Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information.
Marginalized graph autoencoder for graph clustering
In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
Transfer Learning across Networks for Collective Classification
In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks.
