Mohammad Taher Pilehvar
Natural Language Processing · 2013–2018
A researcher in the Science corpus: 9 papers; trusts 369 researchers and is trusted by 58 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 241 citations into it; #966 of 5,688 by trust here, #1,399 by citations
- Artificial Intelligence — 226 citations into it; #4,844 of 35,345 by trust here, #6,911 by citations
- Representation & Self-Supervised Learning — 126 citations into it; #1,185 of 3,145 by trust here, #2,404 by citations
- Evaluation & Benchmarks — 23 citations into it; #781 of 3,147 by trust here, #1,159 by citations
- Information Retrieval & Search — 23 citations into it
- Molecular Biology — 21 citations into it
- Computer Vision and Pattern Recognition — 19 citations into it; #8,919 of 21,575 by trust here, #14,151 by citations
- Information Systems — 16 citations into it
Whom Mohammad Taher Pilehvar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Roberto Navigli
- Tomas Mikolov
- Geoffrey E. Hinton
- Yoshua Bengio
- Richard Socher
- Ilya Sutskever
- Christopher D. Manning
- Diederik P. Kingma
- Alex Krizhevsky
- Sepp Hochreiter
Who trusts Mohammad Taher Pilehvar overall
- José Camacho-Collados
- Dhivya Chandrasekaran
- Roberto Navigli
- Yi Tay
- Yoav Levine
- Timo Schick
- Ignacio Iacobacci
- Ziyang Luo
- Mario Giulianelli
- Thierry Etchegoyhen
Papers from Mohammad Taher Pilehvar's perspective
Ordered by how strongly the researchers Mohammad Taher Pilehvar trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (2018) — 601 citations
- Embeddings for Word Sense Disambiguation: An Evaluation Study (2016) — 274 citations
- SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity (2017) — 161 citations
- WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations (2018) — 50 citations
- From Word to Sense Embeddings: A Survey on Vector Representations of Meaning (2018) — 371 citations
- Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017) — 47 citations
- De-Conflated Semantic Representations (2016) — 96 citations
- SensEmbed: Learning Sense Embeddings for Word and Relational Similarity (2015) — 296 citations
- Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013) — 196 citations