Piero Molino

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 131 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #16,473 of 35,345 by trust here, #3,878 by citations
• Natural Language Processing — 96 citations into it; #3,373 of 5,688 by trust here, #1,343 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #18,749 of 21,575 by trust here, #3,057 by citations
• Model Architectures — 20 citations into it
• Reinforcement Learning — 20 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Information Systems — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Piero Molino trusts overall

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

1. Ilya Sutskever
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yann LeCun
7. Oriol Vinyals
8. Kyunghyun Cho
9. Alex Graves
10. Jimmy Ba

Who trusts Piero Molino overall

1. Yejin Choi
2. Noah A. Smith
3. Y-Lan Boureau
4. Emily Dinan
5. Yulia Tsvetkov
6. Verena Rieser
7. Kai-Wei Chang
8. Sachin Kumar
9. Premkumar Natarajan
10. Nanyun Peng

Papers from Piero Molino's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution (2018) — 1,041 citations
• Plug and Play Language Models: A Simple Approach to Controlled Text Generation (2019) — 1,229 citations
• Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models (2018) — 221 citations
• Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning (2019) — 40 citations
• Modeling Multi-Action Policy for Task-Oriented Dialogues (2019) — 11 citations
• Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems (2018)