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04/23/2022
On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning
Humans are able to identify and categorize novel compositions of known c...
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10/29/2021
Barlow Graph Auto-Encoder for Unsupervised Network Embedding
Network embedding has emerged as a promising research field for network ...
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08/09/2021
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
Heterogeneous Information Network (HIN) embedding refers to the low-dime...
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01/11/2021
Variational Embeddings for Community Detection and Node Representation
In this paper, we study how to simultaneously learn two highly correlate...
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10/22/2020
Metapath- and Entity-aware Graph Neural Network for Recommendation
Due to the shallow structure, classic graph neural networks (GNNs) faile...
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06/19/2020
Compositional Learning of Image-Text Query for Image Retrieval
In this paper, we investigate the problem of retrieving images from a da...
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04/03/2020
Epitomic Variational Graph Autoencoder
Variational autoencoder (VAE) is a widely used generative model for unsu...
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07/24/2019
Counterfactual Learning from Logs for Improved Ranking of E-Commerce Products
Improved search quality enhances users' satisfaction, which directly imp...
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10/08/2018
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations
This work studies the problem of learning appropriate low dimensional im...
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03/12/2018
Clustering with Simultaneous Local and Global View of Data: A message passing based approach
A good clustering algorithm should not only be able to discover clusters...
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08/01/2017
Model-based learning of local image features for unsupervised texture segmentation
Features that capture well the textural patterns of a certain class of i...
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06/14/2017
Alignment Distances on Systems of Bags
Recent research in image and video recognition indicates that many visua...
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08/19/2016
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking
This paper addresses network anomography, that is, the problem of inferr...
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06/12/2015
Robust Structured Low-Rank Approximation on the Grassmannian
Over the past years Robust PCA has been established as a standard tool f...
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03/09/2015
Learning Co-Sparse Analysis Operators with Separable Structures
In the co-sparse analysis model a set of filters is applied to a signal ...
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06/25/2014
A Bimodal Co-Sparse Analysis Model for Image Processing
The success of many computer vision tasks lies in the ability to exploit...
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03/20/2014
On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
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12/19/2013
An Adaptive Dictionary Learning Approach for Modeling Dynamical Textures
Video representation is an important and challenging task in the compute...
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12/17/2013
Co-Sparse Textural Similarity for Image Segmentation
We propose an algorithm for segmenting natural images based on texture a...
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12/13/2013
Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as spa...
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04/19/2013
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 mea...
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03/21/2013
Separable Dictionary Learning
Many techniques in computer vision, machine learning, and statistics rel...
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10/02/2012
Robust PCA and subspace tracking from incomplete observations using L0-surrogates
Many applications in data analysis rely on the decomposition of a data m...
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04/24/2012