Iacer Calixto

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 533 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #12,425 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 123 citations into it
• Computer Vision and Pattern Recognition — 90 citations into it; #8,258 of 21,575 by trust here, #9,722 by citations
• Evaluation & Benchmarks — 52 citations into it
• Multimodal Learning — 32 citations into it; #440 of 1,211 by trust here, #1,004 by citations
• Model Architectures — 18 citations into it
• Large Language Models — 15 citations into it
• Optimization & Training — 12 citations into it

Whom Iacer Calixto trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Kyunghyun Cho
3. Alex Graves
4. Geoffrey E. Hinton
5. Oriol Vinyals
6. Ilya Sutskever
7. Franz Joseph Och
8. Dzmitry Bahdanau
9. Philipp Koehn
10. Alex Krizhevsky

Who trusts Iacer Calixto overall

1. Shaowei Yao
2. Ozan Caglayan
3. Desmond Elliott
4. Longyue Wang
5. Bei Li
6. Xiaojun Wan
7. Loïc Barrault
8. Lucia Specia
9. Pranava Swaroop Madhyastha
10. Zhaopeng Tu

Papers from Iacer Calixto's perspective

Ordered by how strongly the researchers Iacer Calixto trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020) — 69 citations
• Target-Centric Features for Translation Quality Estimation (2014) — 5 citations
• Seeing past words: Testing the cross-modal capabilities of pretrained V&L models on counting tasks (2020) — 53 citations
• Incorporating Global Visual Features into Attention-based Neural Machine Translation. (2017) — 167 citations
• Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation (2017) — 36 citations
• Using Images to Improve Machine-Translating E-Commerce Product Listings. (2017) — 27 citations