Michelle D. Tadmor

Molecular Biology · 2013–2023

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

Subfields

• Molecular Biology — 46 citations into it; #6,522 of 17,773 by trust here, #2,765 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• Biophysics — 14 citations into it; #1,159 of 1,196 by trust here, #142 by citations
• Representation & Self-Supervised Learning — 10 citations into it

Whom Michelle D. Tadmor trusts overall

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

1. L. V. D. van der Maaten
2. Sean Bendall
3. Geoffrey E. Hinton
4. Erin F. Simonds
5. Vin de Silva
6. Frank Rosenbauer
7. Florian Buettner

Who trusts Michelle D. Tadmor overall

1. Eirini Arvaniti
2. F. Alexander Wolf
3. Nicola Pezzotti
4. Manfred Claassen
5. Laleh Haghverdi
6. Allon Wagner
7. F. Mair
8. Fabian J Theis
9. Cole Trapnell
10. Yvan Saeys

Papers from Michelle D. Tadmor's perspective

Ordered by how strongly the researchers Michelle D. Tadmor trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
3. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
4. ImageNet classification with deep convolutional neural networks
5. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Fast unfolding of communities in large networks
10. An Integrated Approach to Uncover Drivers of Cancer (Cell)

Their papers

• AudioPaLM: A Large Language Model That Can Speak and Listen (2023) — 486 citations
• Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B cell Development (2014) — 905 citations
• viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia (2013) — 1,571 citations
• Wishbone identifies bifurcating developmental trajectories from single-cell data (2016) — 570 citations
• Detection of Minimal Residual Disease in B Lymphoblastic Leukemia Using viSNE (2015) — 46 citations
• Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis (2015) — 2,148 citations