Computing and Learning on Combinatorial Data | Paper Citation Record

Observation e09a161a-d271-49e2-afaf-fbcb6a90e72d · outbound

This paper cites Hypha: A framework based on separation of parallelisms to accelerate persistent homology matrix reduc- tion,.

Computing and Learning on Combinatorial Data Hypha: A framework based on separation of parallelisms to accelerate persistent homology matrix reduc- tion,

Reference 1

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Observation 62248231-884f-45a8-829c-7e4c6ff5c65d · outbound

This paper cites Gpu-accelerated computation of Vietoris-Rips persistence barcodes,.

Computing and Learning on Combinatorial Data Gpu-accelerated computation of Vietoris-Rips persistence barcodes,

Reference 2

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This paper cites GPU-accelerated computation of Vietoris-Rips persistence barcodes,.

Computing and Learning on Combinatorial Data GPU-accelerated computation of Vietoris-Rips persistence barcodes,

Reference 3

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This paper cites Approximating 1-wasserstein distance between persis- tence diagrams by graph sparsiﬁcation,.

Computing and Learning on Combinatorial Data Approximating 1-wasserstein distance between persis- tence diagrams by graph sparsiﬁcation,

Reference 4

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This paper cites Geﬂ: Extended ﬁltration learning for graph classiﬁcation,.

Computing and Learning on Combinatorial Data Geﬂ: Extended ﬁltration learning for graph classiﬁcation,

Reference 5

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This paper cites Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking.

Computing and Learning on Combinatorial Data Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking

Reference 6

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This paper cites Expressive higher-order link prediction through hypergraph symmetry breaking,.

Computing and Learning on Combinatorial Data Expressive higher-order link prediction through hypergraph symmetry breaking,

Reference 7

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Computing and Learning on Combinatorial Data Unresolved cited work

Reference 8

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This paper cites Survey article: The real numbers–a survey of constructions,.

Computing and Learning on Combinatorial Data Survey article: The real numbers–a survey of constructions,

Reference 9

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Computing and Learning on Combinatorial Data Unresolved cited work

Reference 10

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Reference 11

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This paper cites General theory of natural equivalences,.

Computing and Learning on Combinatorial Data General theory of natural equivalences,

Reference 12

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This paper cites Acyclic categories,.

Computing and Learning on Combinatorial Data Acyclic categories,

Reference 13

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This paper cites Quiver representations,.

Computing and Learning on Combinatorial Data Quiver representations,

Reference 14

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This paper cites Ueber die theorie der algebraischen formen,.

Computing and Learning on Combinatorial Data Ueber die theorie der algebraischen formen,

Reference 15

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Reference 16

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This paper cites Hoﬀman and R.

Computing and Learning on Combinatorial Data Hoﬀman and R

Reference 17

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Reference 18

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This paper cites Idealtheorie in ringbereichen,.

Computing and Learning on Combinatorial Data Idealtheorie in ringbereichen,

Reference 19

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Computing and Learning on Combinatorial Data Unresolved cited work

Reference 20

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This paper cites The group of isometries of a riemannian manifold,.

Computing and Learning on Combinatorial Data The group of isometries of a riemannian manifold,

Reference 21

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This paper cites Boyd and L.

Computing and Learning on Combinatorial Data Boyd and L

Reference 22

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Reference 23

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This paper cites Causal inference in statistics: An overview,.

Computing and Learning on Combinatorial Data Causal inference in statistics: An overview,

Reference 24

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This paper cites Pearl, Causality.

Computing and Learning on Combinatorial Data Pearl, Causality

Reference 25

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This paper cites Birkhoﬀ, Lattice theory.

Computing and Learning on Combinatorial Data Birkhoﬀ, Lattice theory

Reference 26

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This paper cites Schiﬄer, Quiver representations.

Computing and Learning on Combinatorial Data Schiﬄer, Quiver representations

Reference 27

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This paper cites Steele, The Persistence of Memory (1931) by Salvador Dali (CC BY-NC-ND 2.0).

Computing and Learning on Combinatorial Data Steele, The Persistence of Memory (1931) by Salvador Dali (CC BY-NC-ND 2.0)

Reference 28

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This paper cites Unzerlegbare darstellungen i,.

Computing and Learning on Combinatorial Data Unzerlegbare darstellungen i,

Reference 29

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This paper cites Topological sorting of large networks,.

Computing and Learning on Combinatorial Data Topological sorting of large networks,

Reference 30

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This paper cites Representations of quivers,.

Computing and Learning on Combinatorial Data Representations of quivers,

Reference 31

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This paper cites Morin, Quiver algebras , 2014.

Computing and Learning on Combinatorial Data Morin, Quiver algebras , 2014

Reference 32

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This paper cites Inﬁnite root systems, representations of graphs and invariant theory,.

Computing and Learning on Combinatorial Data Inﬁnite root systems, representations of graphs and invariant theory,

Reference 33

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This paper cites Inﬁnite root systems, representations of graphs and invariant theory, ii,.

Computing and Learning on Combinatorial Data Inﬁnite root systems, representations of graphs and invariant theory, ii,

Reference 34

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This paper cites A multiprocessor system design,.

Computing and Learning on Combinatorial Data A multiprocessor system design,

Reference 35

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This paper cites A bridging model for parallel computation,.

Computing and Learning on Combinatorial Data A bridging model for parallel computation,

Reference 36

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This paper cites Dot: A matrix model for analyzing, optimizing and deploying software for big data analytics in distributed systems,.

Computing and Learning on Combinatorial Data Dot: A matrix model for analyzing, optimizing and deploying software for big data analytics in distributed systems,

Reference 37

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This paper cites Mapreduce: Simpliﬁed data processing on large clusters,.

Computing and Learning on Combinatorial Data Mapreduce: Simpliﬁed data processing on large clusters,

Reference 38

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This paper cites The theory of multidimensional persistence,.

Computing and Learning on Combinatorial Data The theory of multidimensional persistence,

Reference 39

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This paper cites Invariants and metrics for multiparameter persistent homology,.

Computing and Learning on Combinatorial Data Invariants and metrics for multiparameter persistent homology,

Reference 40

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This paper cites Stratifying multiparameter persistent homology,.

Computing and Learning on Combinatorial Data Stratifying multiparameter persistent homology,

Reference 41

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This paper cites Generalized persistence diagrams for persistence modules over posets,.

Computing and Learning on Combinatorial Data Generalized persistence diagrams for persistence modules over posets,

Reference 42

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This paper cites Computing generalized rank invariant for 2- parameter persistence modules via zigzag persistence and its applications,.

Computing and Learning on Combinatorial Data Computing generalized rank invariant for 2- parameter persistence modules via zigzag persistence and its applications,

Reference 43

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Computing and Learning on Combinatorial Data Unresolved cited work

Reference 44

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Computing and Learning on Combinatorial Data The structure of semi-simple algebras,

Reference 45

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Computing and Learning on Combinatorial Data Fulton and J

Reference 46

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Computing and Learning on Combinatorial Data Zigzag persistence,

Reference 47

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Computing and Learning on Combinatorial Data Strang, Linear algebra and its applications

Reference 48

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This paper cites Elder-rule-staircodes for augmented metric spaces,.

Computing and Learning on Combinatorial Data Elder-rule-staircodes for augmented metric spaces,

Reference 49

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Computing and Learning on Combinatorial Data S. eilenberg homological algebra,

Reference 50

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Computing and Learning on Combinatorial Data Topology and data,

Reference 51

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This paper cites Coverage in sensor networks via persistent homology,.

Computing and Learning on Combinatorial Data Coverage in sensor networks via persistent homology,

Reference 52

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This paper cites A topological paradigm for hippocampal spatial map formation using persistent homology,.

Computing and Learning on Combinatorial Data A topological paradigm for hippocampal spatial map formation using persistent homology,

Reference 53

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This paper cites Finding the homology of submanifolds with high conﬁdence from random samples,.

Computing and Learning on Combinatorial Data Finding the homology of submanifolds with high conﬁdence from random samples,

Reference 54

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This paper cites A study on validating non-linear dimensionality reduction using persistent homology,.

Computing and Learning on Combinatorial Data A study on validating non-linear dimensionality reduction using persistent homology,

Reference 55

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Computing and Learning on Combinatorial Data Deep learning with topological signatures,

Reference 56

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Computing and Learning on Combinatorial Data A roadmap for the computation of persistent homology,

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Computing and Learning on Combinatorial Data Javaplex tutorial,

Reference 58

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Computing and Learning on Combinatorial Data Phat–persistent homology algorithms toolbox,

Reference 59

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Computing and Learning on Combinatorial Data Morozov, Dionysus software , 2017

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This paper cites Distributed computation of persistent homology,.

Computing and Learning on Combinatorial Data Distributed computation of persistent homology,

Reference 61

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This paper cites Bauer, Ripser: Eﬃcient computation of vietorisrips persistence barcodes , 2018.

Computing and Learning on Combinatorial Data Bauer, Ripser: Eﬃcient computation of vietorisrips persistence barcodes , 2018

Reference 62

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This paper cites Matroid Filtrations and Computational Persistent Homology.

Computing and Learning on Combinatorial Data Matroid Filtrations and Computational Persistent Homology

Reference 63

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This paper cites A new data structure for cumulative frequency tables,.

Computing and Learning on Combinatorial Data A new data structure for cumulative frequency tables,

Reference 64

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This paper cites Cramming more components onto integrated circuits,.

Computing and Learning on Combinatorial Data Cramming more components onto integrated circuits,

Reference 65

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This paper cites Design of ion-implanted mosfet’s with very small physical dimensions,.

Computing and Learning on Combinatorial Data Design of ion-implanted mosfet’s with very small physical dimensions,

Reference 66

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This paper cites Edelsbrunner and J.

Computing and Learning on Combinatorial Data Edelsbrunner and J

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Computing and Learning on Combinatorial Data Clear and compress: Computing persistent homology in chunks,

Reference 68

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Computing and Learning on Combinatorial Data Persistent homology computation with a twist,

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Computing and Learning on Combinatorial Data Vines and vineyards by updat- ing persistence in linear time,

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Computing and Learning on Combinatorial Data Parallel multi-scale reduction of persistent homology filtrations

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Computing and Learning on Combinatorial Data The nonlinear statistics of high-contrast patches in natural images,

Reference 73

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Computing and Learning on Combinatorial Data Introduction to alpha shapes,

Reference 74

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Computing and Learning on Combinatorial Data Loops skewing: The wavefront method revisited,

Reference 75

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Computing and Learning on Combinatorial Data Peerwave: Exploiting wavefront parallelism on gpus with peer-sm synchronization,

Reference 76

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Computing and Learning on Combinatorial Data Highly eﬃcient compensation- based parallelism for wavefront loops on gpus,

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Computing and Learning on Combinatorial Data The pareto, zipf and other power laws,

Reference 78

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Computing and Learning on Combinatorial Data The tail at scale,

Reference 79

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Computing and Learning on Combinatorial Data Fibonacci heaps and their uses in improved network optimization algorithms,

Reference 80

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Computing and Learning on Combinatorial Data The yin and yang of processing data warehousing queries on gpu devices,

Reference 81

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Computing and Learning on Combinatorial Data Persistent cohomology and circular coordinates,

Reference 82

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Computing and Learning on Combinatorial Data Dualities in persistent (co) homology,

Reference 83

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Computing and Learning on Combinatorial Data Austria, Phat (persistent homology algorithm toolbox), v1.5 , 2017

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Computing and Learning on Combinatorial Data Custinger: Supporting dynamic graph algorithms for gpus,

Reference 85

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Computing and Learning on Combinatorial Data Accelerating dynamic graph analytics on gpus,

Reference 86

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Computing and Learning on Combinatorial Data Hornet: An eﬃcient data struc- ture for dynamic sparse graphs and matrices on gpus,

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Computing and Learning on Combinatorial Data Faimgraph: High performance management of fully-dynamic graphs under tight memory constraints on the gpu,

Reference 88

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Computing and Learning on Combinatorial Data Performance enhance- ment of a computational persistent homology package,

Reference 89

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Computing and Learning on Combinatorial Data Morse theory for ﬁltrations and eﬃcient computation of persistent homology,

Reference 90

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Computing and Learning on Combinatorial Data GUDHI Editorial Board,

Reference 91

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Computing and Learning on Combinatorial Data The compressed annotation matrix: An eﬃcient data structure for computing persistent cohomology,

Reference 92

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Computing and Learning on Combinatorial Data Computing topological persistence for simplicial maps,

Reference 93

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Computing and Learning on Combinatorial Data Accelerating pathol- ogy image data cross-comparison on cpu-gpu hybrid systems,

Reference 94

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Computing and Learning on Combinatorial Data Throughput-oriented gpu memory allocation,

Reference 95

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Computing and Learning on Combinatorial Data Mega-kv: A case for gpus to maximize the throughput of in-memory key-value stores,

Reference 96

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Computing and Learning on Combinatorial Data Persistence homology of networks: Methods and applications,

Reference 97

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Computing and Learning on Combinatorial Data Computing persistent homology of directed flag complexes

Reference 98

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Computing and Learning on Combinatorial Data Proof of bijection for combinatorial number system

Reference 99

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Computing and Learning on Combinatorial Data Unresolved cited work

Reference 100

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