Philipp Berens

Molecular Biology · 2009–2024

A researcher in the Science corpus: 13 papers; trusts 75 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 78 citations into it; #961 of 17,773 by trust here, #8,480 by citations
• Artificial Intelligence — 59 citations into it; #5,578 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Representation & Self-Supervised Learning — 50 citations into it; #1,401 of 3,145 by trust here, #2,330 by citations
• Biophysics — 36 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Statistics and Probability — 12 citations into it
• Computational Mechanics — 9 citations into it

Whom Philipp Berens trusts overall

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

1. L. V. D. van der Maaten
2. Nicholas I. Fisher
3. Geoffrey E. Hinton
4. S. Jammalamadaka
5. George C. Linderman
6. Dmitry Kobak
7. Leland McInnes
8. Yoshua Bengio
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Philipp Berens overall

1. B. Bruce
2. Luís Aguiar-Conraria
3. Fatemeh Karimi
4. E. Balestrieri
5. Aisling Ní Annaidh
6. Dmitry Kobak
7. Timo van Kerkoerle
8. R. Bradford
9. George C. Linderman
10. Raphael Vallat

Papers from Philipp Berens's perspective

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

Their papers

• Leveraging uncertainty information from deep neural networks for disease detection (2016) — 553 citations
• The art of using t-SNE for single-cell transcriptomics (2018) — 1,041 citations
• CircStat: AMATLABToolbox for Circular Statistics (2009) — 2,018 citations
• Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq (2015) — 570 citations
• Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations (2019) — 46 citations
• The landscape of biomedical research (2024)
• Correction to: Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations (2020)
• A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum (2020) — 26 citations
• On the ethics of algorithmic decision-making in healthcare (2019) — 398 citations
• Sparse activity of identified dentate granule cells during spatial exploration (2016) — 144 citations
• The functional diversity of retinal ganglion cells in the mouse (2016) — 893 citations
• Principles of connectivity among morphologically defined cell types in adult neocortex (2015) — 832 citations
• Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness (2014) — 200 citations