Andrea Gesmundo

Artificial Intelligence · 2009–2025

A researcher in the Science corpus: 12 papers; trusts 650 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 232 citations into it; #5,721 of 35,345 by trust here, #2,415 by citations
• Computer Vision and Pattern Recognition — 135 citations into it; #6,890 of 21,575 by trust here, #2,287 by citations
• Natural Language Processing — 89 citations into it; #1,914 of 5,688 by trust here, #580 by citations
• Model Architectures — 68 citations into it
• Optimization & Training — 57 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Reinforcement Learning — 29 citations into it
• Evaluation & Benchmarks — 25 citations into it

Whom Andrea Gesmundo trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Quoc V. Le
4. Alex Krizhevsky
5. Barret Zoph
6. Diederik P. Kingma
7. Kaiming He
8. Yann LeCun
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Andrea Gesmundo overall

1. Diego Marcheggiani
2. Bernd Bohnet
3. Ivan Titov
4. James Henderson
5. Swabha Swayamdipta
6. Renrui Zhang
7. Jonas Pfeiffer
8. Rodrigo Nogueira
9. Colin White
10. Zixuan Ke

Papers from Andrea Gesmundo's perspective

Ordered by how strongly the researchers Andrea Gesmundo 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Parameter-Efficient Transfer Learning for NLP (2019) — 6,877 citations
• Ask the Right Questions: Active Question Reformulation with Reinforcement Learning (2017) — 177 citations
• Transfer Learning with Neural AutoML (2018) — 118 citations
• A Latent Variable Model of Synchronous Syntactic-Semantic Parsing for Multiple Languages (2009) — 55 citations
• Fast Task-Aware Architecture Inference (2019) — 18 citations
• Evolutionary-Neural Hybrid Agents for Architecture Search (2018) — 44 citations
• Gumbel-Matrix Routing for Flexible Multi-task Learning (2019) — 13 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Scaling Up Models and Data with t5x and seqio (2022) — 224 citations
• muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems (2022) — 22 citations
• Routing Networks with Co-training for Continual Learning (2020) — 15 citations
• Transfer Automatic Machine Learning (2018) — 10 citations