Harm Derksen

Geometry and Topology · 2000–2017

A researcher in the Science corpus: 14 papers; trusts 35 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Geometry and Topology — 52 citations into it; #477 of 7,422 by trust here, #230 by citations
• Algebra and Number Theory — 42 citations into it; #124 of 2,864 by trust here, #48 by citations
• Discrete Mathematics and Combinatorics — 28 citations into it; #502 of 2,017 by trust here, #199 by citations
• Statistical and Nonlinear Physics — 20 citations into it; #818 of 10,127 by trust here, #672 by citations
• Mathematical Physics — 15 citations into it; #2,703 of 8,562 by trust here, #2,582 by citations
• Computational Theory and Mathematics — 11 citations into it; #4,809 of 12,182 by trust here, #3,339 by citations
• Computer Vision and Pattern Recognition — 5 citations into it; #9,306 of 21,575 by trust here, #11,498 by citations

Whom Harm Derksen trusts overall

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

1. Sergey Fomin
2. Philippe Caldero
3. Andrei Zelevinsky
4. Daniel Simson
5. Jerzy Weyman
6. David E. Speyer
7. B. Keller
8. Osamu Iyama
9. B. Marsh
10. Andrzej Skowronski

Who trusts Harm Derksen overall

1. B. Keller
2. Osamu Iyama
3. Gustavo Jasso
4. Pierre-Guy Plamondon
5. Eric Katz
6. Jerzy Weyman
7. Laurent Demonet
8. Bernard Leclerc
9. Sophie Morier-Genoud
10. Marcelo Aguiar

Papers from Harm Derksen's perspective

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

1. Cluster algebras I: Foundations
2. Generalized associahedra via quiver representations
3. Cluster algebras II: Finite type classification
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression (2007) — 537 citations
• Valuative invariants for polymatroids (2009) — 87 citations
• Computational Invariant Theory (2002) — 562 citations
• An Introduction to Quiver Representations (2017) — 103 citations
• Quivers with potentials and their representations II: Applications to cluster algebras (2009) — 489 citations
• General Presentations of Algebras (2009) — 88 citations
• Quivers with potentials and their representations I: Mutations (2007) — 529 citations
• The combinatorics of quiver representations (2006) — 93 citations
• A Skolem–Mahler–Lech theorem in positive characteristic and finite automata (2005) — 63 citations
• On the Canonical Decomposition of Quiver Representations (2002) — 76 citations
• Generalized quivers associated to reductive groups (2002) — 66 citations
• A sharp bound for the Castelnuovo–Mumford regularity of subspace arrangements (2001) — 74 citations
• Polynomial bounds for rings of invariants (2000) — 69 citations
• Semi-invariants of quivers and saturation for Littlewood-Richardson coefficients (2000) — 242 citations