Aleksandr Drozd
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 4 papers; trusts 74 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 60 citations into it; #16,114 of 35,345 by trust here, #19,439 by citations
- Natural Language Processing — 50 citations into it; #1,990 of 5,688 by trust here, #3,369 by citations
- Representation & Self-Supervised Learning — 34 citations into it; #1,232 of 3,145 by trust here, #2,148 by citations
- Computer Vision and Pattern Recognition — 12 citations into it
- Evaluation & Benchmarks — 10 citations into it
Whom Aleksandr Drozd trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tomas Mikolov
- Omer Levy
- Peter D. Turney
- Marco Baroni
- Yoav Goldberg
- Jeffrey Pennington
- Richard Socher
- Yoshua Bengio
- Geoffrey E. Hinton
- Ilya Sutskever
Who trusts Aleksandr Drozd overall
- Malvina Nissim
- Kiho Park
- Anna Rogers
- Zied Bouraoui
- David Rozado
- Orestis Papakyriakopoulos
- Rik van Noord
- Ivan Vulic
- Yo Joong Choe
- José Camacho-Collados
Papers from Aleksandr Drozd's perspective
Ordered by how strongly the researchers Aleksandr Drozd trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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
- Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. (2016) — 275 citations
- Outliers Dimensions that Disrupt Transformers Are Driven by Frequency (2022) — 65 citations
- The (too Many) Problems of Analogical Reasoning with Word Vectors (2017) — 88 citations
- Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen (2016) — 174 citations