Noah Fiedel
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 19 papers; trusts 105 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 470 citations into it; #11,196 of 35,345 by trust here, #819 by citations
- Natural Language Processing — 240 citations into it; #2,749 of 5,688 by trust here, #933 by citations
- Computer Vision and Pattern Recognition — 186 citations into it; #18,352 of 21,575 by trust here, #842 by citations
- Large Language Models — 124 citations into it; #612 of 799 by trust here, #89 by citations
- Evaluation & Benchmarks — 103 citations into it
- Model Architectures — 76 citations into it
- Safety, Robustness & Interpretability — 52 citations into it
- Optimization & Training — 36 citations into it
Whom Noah Fiedel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Noam Shazeer
- Ilya Sutskever
- Yoshua Bengio
- Diederik P. Kingma
- Alex Graves
- Ashish Vaswani
- Alex Krizhevsky
- Oriol Vinyals
- Kaiming He
Who trusts Noah Fiedel overall
Papers from Noah Fiedel's perspective
Ordered by how strongly the researchers Noah Fiedel trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- PaLM: Scaling Language Modeling with Pathways (2022) — 8,214 citations
- Towards a Human-like Open-Domain Chatbot (2020) — 1,033 citations
- Do Transformer Modifications Transfer Across Implementations and Applications? (2021) — 138 citations
- WT5?! Training Text-to-Text Models to Explain their Predictions (2020) — 219 citations
- TALM: Tool Augmented Language Models (2022) — 207 citations
- Understanding HTML with Large Language Models (2022) — 95 citations
- TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (2017) — 478 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
- TensorFlow-Serving: Flexible, High-Performance ML Serving (2017) — 362 citations
- Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Gemma 3 Technical Report (2025) — 1,621 citations
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (2023) — 337 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
- Position: Levels of AGI for Operationalizing Progress on the Path to AGI (2023) — 113 citations
- Scaling Up Models and Data with t5x and seqio (2022) — 224 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
- Factorized Deep Retrieval and Distributed TensorFlow Serving (2018)