Joshua T. Vogelstein

Artificial Intelligence · 2009–2023

A researcher in the Science corpus: 21 papers; trusts 43 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 137 citations into it
• Computer Vision and Pattern Recognition — 70 citations into it
• Representation & Self-Supervised Learning — 64 citations into it
• Statistics and Probability — 44 citations into it; #9,170 of 12,932 by trust here, #5,661 by citations
• Statistical and Nonlinear Physics — 34 citations into it
• Statistical Learning Theory — 23 citations into it
• Natural Language Processing — 18 citations into it
• Model Architectures — 13 citations into it

Whom Joshua T. Vogelstein trusts overall

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

1. Vladimir Naumovich Vapnik
2. Arthur Gretton
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Gido M. van de Ven
8. Diederik P. Kingma
9. Jerome H. Friedman
10. R. Caruana

Who trusts Joshua T. Vogelstein overall

1. Hayden S. Helm
2. Laurence Aitchison
3. Johannes Friedrich
4. Gido M. van de Ven
5. Zhaoxi Mu
6. Alberto Greco
7. Liron Sheintuch
8. Alexey A. Gritsenko
9. Marius Pachitariu
10. Pengcheng Zhou

Papers from Joshua T. Vogelstein's perspective

Ordered by how strongly the researchers Joshua T. Vogelstein trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The exact equivalence of distance and kernel methods in hypothesis testing (2018) — 50 citations
• Biological underpinnings for lifelong learning machines (2022) — 294 citations
• A partition-based similarity for classification distributions (2020) — 6 citations
• Statistical Inference on Random Dot Product Graphs: a Survey (2017) — 290 citations
• Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis (2023)
• Discovering a change point in a time series of organoid networks via the iso-mirror (2023)
• The connectome of an insect brain (2022)
• Omnidirectional Transfer for Quasilinear Lifelong Learning (2021)
• Towards a theory of out-of-distribution learning (2021)
• Dynamic Silos: Modularity in intra-organizational communication networks before and during the Covid-19 pandemic (2021)
• Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic (2021)
• A general approach to progressive learning (2020) — 10 citations
• Modern Machine Learning: Partition & Vote (2020)
• GraSPy: Graph Statistics in Python (2019)
• NeuroStorm: Accelerating Brain Science Discovery in the Cloud (2018) — 0 citations
• Detection and localization of surgically resectable cancers with a multi-analyte blood test (2018) — 2,397 citations
• Whole-brain serial-section electron microscopy in larval zebrafish (2017) — 327 citations
• Grand Challenges for Global Brain Sciences (2016)
• To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery (2016)
• Saturated Reconstruction of a Volume of Neocortex. (2015) — 1,027 citations