Boris Katz
Artificial Intelligence · 2003–2020
A researcher in the Science corpus: 7 papers; trusts 176 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #34,038 of 35,345 by trust here, #14,181 by citations
- Computer Vision and Pattern Recognition — 46 citations into it; #20,858 of 21,575 by trust here, #6,206 by citations
- Natural Language Processing — 39 citations into it; #5,336 of 5,688 by trust here, #3,235 by citations
- Evaluation & Benchmarks — 19 citations into it; #2,969 of 3,147 by trust here, #1,399 by citations
- Multimodal Learning — 11 citations into it
- Safety, Robustness & Interpretability — 9 citations into it
- Information Retrieval & Search — 7 citations into it; #982 of 1,022 by trust here, #637 by citations
- Representation & Self-Supervised Learning — 7 citations into it
Whom Boris Katz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Ellen M. Voorhees
- Karen Simonyan
- Ross B. Girshick
Who trusts Boris Katz overall
Papers from Boris Katz's perspective
Ordered by how strongly the researchers Boris Katz trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models (2019) — 692 citations
- Grounding language acquisition by training semantic parsers using captioned videos (2018) — 17 citations
- Measuring Social Biases in Grounded Vision and Language Embeddings (2020) — 73 citations
- Quantitative evaluation of passage retrieval algorithms for question answering (2003) — 355 citations
- Learning to Answer Questions from Wikipedia Infoboxes (2016) — 19 citations
- The role of context in question answering systems (2003) — 73 citations
- What Makes a Good Answer? The Role of Context in Question Answering (2003) — 137 citations