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Mahdieh Soleymani Baghshah
Sharif University of Technology
35Publications
9H-index
292Citations
Publications 35
Newest
Published in arXiv: Learning
Ehsan Montahaei1
Estimated H-index: 1
,
Danial Alihosseini (Sharif University of Technology), Mahdieh Soleymani Baghshah9
Estimated H-index: 9
(Sharif University of Technology)
Although GAN-based methods have received many achievements in the last few years, they have not been such successful in generating discrete data. The most important challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. Despite several attempts done to alleviate this problem, none of the existing GAN-based methods has improved the performance of text generation (using measures that evaluate both the qu...
Published in arXiv: Learning
Mahsa Ghorbani1
Estimated H-index: 1
,
Mahdieh Soleymani Baghshah9
Estimated H-index: 9
,
Hamid R. Rabiee17
Estimated H-index: 17
(Sharif University of Technology)
Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been r...
Published on Jan 1, 2019in arXiv: Learning
Ehsan Montahaei1
Estimated H-index: 1
,
Danial Alihosseini (Sharif University of Technology), Mahdieh Soleymani Baghshah9
Estimated H-index: 9
(Sharif University of Technology)
Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduc...
Published on Feb 1, 2019in IEEE Transactions on Knowledge and Data Engineering 3.86
Amirhossein Akbarnejad1
Estimated H-index: 1
(Sharif University of Technology),
Mahdieh Soleymani Baghshah9
Estimated H-index: 9
(Sharif University of Technology)
Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignmen...
Zeinab Golgooni (Sharif University of Technology), Sara Mirsadeghi1
Estimated H-index: 1
(Royan Institute)
+ -3 AuthorsHamid R. Rabiee17
Estimated H-index: 17
(Sharif University of Technology)
An early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro proarrhythmia assay and deep learning techniques. We aimed to develop a method to automatically detect irregular beating rhythm of field potentials recorded from human pluripotent stem cells (hPSC) derived cardiomyocytes (hPSC-CM) by multi-electrode array (MEA) system. We included field potentials from 380 experiments, which were labeled as normal or arrhythmic by electrophysiology expert...
Published on Jan 1, 2018in arXiv: Learning
Ehsan Montahaei1
Estimated H-index: 1
,
Mahsa Ghorbani1
Estimated H-index: 1
+ 1 AuthorsHamid R. Rabiee17
Estimated H-index: 17
Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper, we propose an adversarial framework for classifier training that can also handle imbalanced data. Indeed, a network is trained via an adversarial approach to give weights to samples of the majority class such that the obtained classification problem becomes more challenging for the discriminator and thus boosts its c...
Published on Jan 1, 2018in arXiv: Learning
Mahsa Ghorbani1
Estimated H-index: 1
,
Mahdieh Soleymani Baghshah9
Estimated H-index: 9
,
Hamid R. Rabiee17
Estimated H-index: 17
Recently, graph embedding emerges as an effective approach for graph analysis tasks such as node classification and link prediction. The goal of network embedding is to find low dimensional representation of graph nodes that preserves the graph structure. Since there might be signals on nodes as features, recent methods like Graph Convolutional Networks (GCNs) try to consider node signals besides the node relations. On the other hand, multi-layered graph analysis has been received much attention...
Published on Nov 15, 2017
Seyed Mahdi Roostaiyan1
Estimated H-index: 1
(Sharif University of Technology),
Ehsan Imani , Mahdieh Soleymani Baghshah9
Estimated H-index: 9
(Sharif University of Technology)
Published on May 1, 2017 in ICEE (Iranian Conference on Electrical Engineering)
Ramtin Yazdanian (Sharif University of Technology), Seyed Mohsen Shojaee2
Estimated H-index: 2
(Sharif University of Technology),
Mahdieh Soleymani Baghshah9
Estimated H-index: 9
(Sharif University of Technology)
Recently, the problem of integrating side information about classes has emerged in the learning settings like zero-shot learning. Although using multiple sources of information about the input space has been investigated in the last decade and many multi-view and multi-modal learning methods have already been introduced, the attribute learning for classes (output space) is a new problem that has been attended in the last few years. In this paper, we propose an attribute learning method that can ...
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