Daniel A. Abolafia

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 4 papers; trusts 70 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #17,289 of 35,345 by trust here, #7,346 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Model Architectures — 32 citations into it
• Optimization & Training — 23 citations into it
• Natural Language Processing — 21 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it; #1,285 of 1,525 by trust here, #648 by citations
• Reinforcement Learning — 9 citations into it
• Statistical Learning Theory — 8 citations into it

Whom Daniel A. Abolafia trusts overall

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

1. Ronald J. Williams
2. Geoffrey E. Hinton
3. Quoc V. Le
4. Sepp Hochreiter
5. Yoshua Bengio
6. Ilya Sutskever
7. Alex Krizhevsky
8. Alex Graves
9. Oriol Vinyals
10. Diederik P. Kingma

Who trusts Daniel A. Abolafia overall

1. Brenden K. Petersen
2. Catherine Wong
3. Abhinav Verma
4. Greg Yang
5. Mikel Landajuela
6. Kevin Ellis
7. Vijayaraghavan Murali
8. Chen Liang

Papers from Daniel A. Abolafia's perspective

Ordered by how strongly the researchers Daniel A. Abolafia 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Sensitivity and Generalization in Neural Networks: an Empirical Study (2018) — 514 citations
• Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes (2018) — 331 citations
• Neural Program Synthesis with Priority Queue Training (2018) — 69 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations