Pablo Piantanida

Artificial Intelligence · 2011–2021

A researcher in the Science corpus: 5 papers; trusts 159 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #30,558 of 35,345 by trust here, #29,412 by citations
• Natural Language Processing — 109 citations into it
• Evaluation & Benchmarks — 74 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #2,788 of 3,145 by trust here, #2,957 by citations
• Large Language Models — 13 citations into it
• Computer Networks and Communications — 9 citations into it
• Electrical and Electronic Engineering — 7 citations into it

Whom Pablo Piantanida trusts overall

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

1. Geoffrey E. Hinton
2. Judea Pearl
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ilya Sutskever
8. Chin-Yew Lin
9. Yann LeCun
10. Alexandre B. Tsybakov

Who trusts Pablo Piantanida overall

1. Yanai Elazar
2. Brij Mohan Lal Srivastava

Papers from Pablo Piantanida's perspective

Ordered by how strongly the researchers Pablo Piantanida 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? (2020) — 240 citations
• Learning Anonymized Representations with Adversarial Neural Networks (2018) — 61 citations
• Secure Multiterminal Source Coding With Side Information at the Eavesdropper (2011) — 75 citations
• Automatic Text Evaluation through the Lens of Wasserstein Barycenters (2021) — 44 citations
• InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation (2021) — 52 citations