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Found 28 papers, 5 papers with code
On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning
The task in Compositional Zero-Shot learning (CZSL) is to learn composition of primitive concepts, i. e. objects and states, in such a way that even their novel compositions can be zero-shot classified.
Barlow Graph Auto-Encoder for Unsupervised Network Embedding
Motivated by this, we propose Barlow Graph Auto-Encoder, a simple yet effective architecture for learning network embedding.
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification.
Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
VECoDeR - Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
Metapath- and Entity-aware Graph Neural Network for Recommendation
In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures.
Ranked #1 on
Link Prediction
on MovieLens 25M
Compositional Learning of Image-Text Query for Image Retrieval
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.
Ranked #1 on
Image Retrieval with Multi-Modal Query
on FashionIQ
Epitomic Variational Graph Autoencoder
Variational autoencoder (VAE) is a widely used generative model for learning latent representations.
Mend The Learning Approach, Not the Data: Insights for Ranking E-Commerce Products
In the literature, it is proposed to employ user feedback (such as clicks, add-to-basket (AtB) clicks and orders) to generate relevance judgments.
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations
We propose a generic algorithmic framework, which leverages two classic representation learning paradigms, i. e., sparse representation and the trace quotient criterion.
Extended Affinity Propagation: Global Discovery and Local Insights
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities.
Model-based learning of local image features for unsupervised texture segmentation
We note that the features can be learned from a small set of images, from a single image, or even from image patches.
Alignment Distances on Systems of Bags
Recent research in image and video recognition indicates that many visual processes can be thought of as being generated by a time-varying generative model.
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking
This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows.
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations
This paper presents an algorithm that allows to learn low dimensional representations of images in an unsupervised manner.
Robust Structured Low-Rank Approximation on the Grassmannian
Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers.
Learning Co-Sparse Analysis Operators with Separable Structures
In many applications, it is also required that the filter responses are obtained in a timely manner, which can be achieved by filters with a separable structure.
A Bimodal Co-Sparse Analysis Model for Image Processing
In this paper, we propose a co-sparse analysis model that is able to capture the interdependency of two image modalities.
Separable Cosparse Analysis Operator Learning
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields.
On The Sample Complexity of Sparse Dictionary Learning
The main goal of this paper is to provide a sample complexity estimate that controls to what extent the empirical average deviates from the cost function.
An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures
Video representation is an important and challenging task in the computer vision community.
Co-Sparse Textural Similarity for Image Segmentation
We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation within a convex multilabel optimization framework.
Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by concatenating high-dimensional vectors from a training collection.
Separable Dictionary Learning
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary.
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene.
pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video
An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects.
Robust PCA and subspace tracking from incomplete observations using L0-surrogates
Many applications in data analysis rely on the decomposition of a data matrix into a low-rank and a sparse component.
Analysis Operator Learning and Its Application to Image Reconstruction
Our method is based on an $\ell_p$-norm minimization on the set of full rank matrices with normalized columns.
