Daniel Duckworth
Computer Vision and Pattern Recognition · with Sajjadi · 2010–2023
A researcher in the Science corpus: 13 papers; trusts 318 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 238 citations into it; #8,681 of 21,575 by trust here, #2,494 by citations
- Artificial Intelligence — 226 citations into it; #21,436 of 35,345 by trust here, #4,393 by citations
- Natural Language Processing — 135 citations into it; #3,780 of 5,688 by trust here, #2,106 by citations
- Generative Models — 42 citations into it
- Model Architectures — 33 citations into it
- Large Language Models — 32 citations into it
- Optimization & Training — 25 citations into it
- Safety, Robustness & Interpretability — 21 citations into it
Whom Daniel Duckworth trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Oriol Vinyals
- Ashish Vaswani
- Kaiming He
- Yann LeCun
- Alex Graves
Who trusts Daniel Duckworth overall
- J. Yang
- Laurence Aitchison
- Jooyoung Lee
- Hugh Zhang
- Lei He
- Kalpesh Krishna
- Hayden S. Helm
- Jonathan T. Barron
- Vinu Sankar Sadasivan
- Andrea Madotto
Papers from Daniel Duckworth's perspective
Ordered by how strongly the researchers Daniel Duckworth trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- PaLM-E: An Embodied Multimodal Language Model (2023) — 2,915 citations
- NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections (2020) — 1,885 citations
- Kubric: A scalable dataset generator (2022) — 467 citations
- Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset (2019) — 246 citations
- Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations (2021) — 260 citations
- Object Scene Representation Transformer (2022) — 120 citations
- Trading Off Diversity and Quality in Natural Language Generation (2020) — 133 citations
- Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019) — 524 citations
- Stochastic natural gradient descent draws posterior samples in function space (2018) — 8 citations
- Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations (2010) — 268 citations
- Parallel Scheduled Sampling (2019) — 24 citations
- Human and Automatic Detection of Generated Text (2019) — 15 citations
- Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning (2019) — 15 citations