Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Kleinsteuber, Martin"'
Humans are able to identify and categorize novel compositions of known concepts. 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 compositi
Externí odkaz:
http://arxiv.org/abs/2204.11848
Network embedding has emerged as a promising research field for network analysis. Recently, an approach, named Barlow Twins, has been proposed for self-supervised learning in computer vision by applying the redundancy-reduction principle to the embed
Externí odkaz:
http://arxiv.org/abs/2110.15742
Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables dow
Externí odkaz:
http://arxiv.org/abs/2108.03953
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i.e., community detection and node representation learning. We propose an efficient generative model called VECoDeR for jointly learning Variational Em
Externí odkaz:
http://arxiv.org/abs/2101.03885
Autor:
Anwaar, Muhammad Umer, Han, Zhiwei, Arumugaswamy, Shyam, Khan, Rayyan Ahmad, Weber, Thomas, Qiu, Tianming, Shen, Hao, Liu, Yuanting, Kleinsteuber, Martin
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture cri
Externí odkaz:
http://arxiv.org/abs/2010.11793
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired mo
Externí odkaz:
http://arxiv.org/abs/2006.11149
Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of
Externí odkaz:
http://arxiv.org/abs/2004.01468
Improved search quality enhances users' satisfaction, which directly impacts sales growth of an E-Commerce (E-Com) platform. Traditional Learning to Rank (LTR) algorithms require relevance judgments on products. In E-Com, getting such judgments poses
Externí odkaz:
http://arxiv.org/abs/1907.10409
This work studies the problem of learning appropriate 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
Externí odkaz:
http://arxiv.org/abs/1810.03523
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities. Extended Affinity Propagation is developed by modifying Affinity Propagation such that the desirable features of Affinity Propagation, e.g., exempla
Externí odkaz:
http://arxiv.org/abs/1803.04459