Oren Melamud
Natural Language Processing · 2015–2016
A researcher in the Science corpus: 4 papers; trusts 105 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 75 citations into it; #1,551 of 5,688 by trust here, #2,263 by citations
- Artificial Intelligence — 64 citations into it; #7,152 of 35,345 by trust here, #10,319 by citations
- Representation & Self-Supervised Learning — 35 citations into it; #928 of 3,145 by trust here, #1,358 by citations
- Evaluation & Benchmarks — 10 citations into it
- Computational Linguistics & Semantics — 6 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Oren Melamud trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Omer Levy
- Geoffrey E. Hinton
- Tomas Mikolov
- Diederik P. Kingma
- Yoshua Bengio
- Yoav Goldberg
- Diana McCarthy
- Richard Socher
- Ilya Sutskever
- Christopher D. Manning
Who trusts Oren Melamud overall
- Matthew E. Peters
- José Camacho-Collados
- Mohammad Taher Pilehvar
- Zeynep Akkalyoncu Yilmaz
- Roberto Navigli
- Ido Dagan
- Omer Levy
- Jacob Goldberger
- Alessandro Raganato
- Yogarshi Vyas
Papers from Oren Melamud's perspective
Ordered by how strongly the researchers Oren Melamud 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- context2vec: Learning Generic Context Embedding with Bidirectional LSTM (2016) — 514 citations
- A Simple Word Embedding Model for Lexical Substitution (2015) — 113 citations
- The Role of Context Types and Dimensionality in Learning Word Embeddings (2016) — 126 citations
- Modeling Word Meaning in Context with Substitute Vectors (2015) — 50 citations