Federico Bianchi

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 12 papers; trusts 177 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #10,227 of 35,345 by trust here, #7,858 by citations
• Natural Language Processing — 138 citations into it; #2,265 of 5,688 by trust here, #2,955 by citations
• Safety, Robustness & Interpretability — 122 citations into it; #955 of 2,251 by trust here, #1,349 by citations
• Computer Vision and Pattern Recognition — 85 citations into it
• Large Language Models — 48 citations into it
• Evaluation & Benchmarks — 31 citations into it; #3,029 of 3,147 by trust here, #2,971 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• Information Systems — 24 citations into it

Whom Federico Bianchi trusts overall

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

1. Geoffrey E. Hinton
2. Shachar Mirkin
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Emily M. Bender
8. Tomas Mikolov
9. Kaiming He
10. Yann LeCun

Who trusts Federico Bianchi overall

1. Dirk Hovy
2. Maarten Grootendorst
3. Debora Nozza
4. Emily Dinan
5. Eduard H. Hovy
6. Yan Xu
7. Lingjiao Chen
8. Gavin Abercrombie
9. A. S. Bergman
10. Zeerak Talat

Papers from Federico Bianchi's perspective

Ordered by how strongly the researchers Federico Bianchi 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• When and why vision-language models behave like bags-of-words, and what to do about it? (2022) — 670 citations
• HONEST: Measuring Hurtful Sentence Completion in Language Models (2021) — 194 citations
• “You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases (2020) — 60 citations
• On the Gap between Adoption and Understanding in NLP (2021) — 25 citations
• Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals (2022) — 73 citations
• A visual–language foundation model for pathology image analysis using medical Twitter (2023) — 826 citations
• XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models (2023) — 447 citations
• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale (2022) — 535 citations
• Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions (2023) — 413 citations
• Pipelines for Social Bias Testing of Large Language Models (2022)
• Can You Translate that into Man? Commercial Machine Translation Systems Include Stylistic Biases (2020)
• Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2020) — 408 citations