On Design Choices in Similarity-Preserving Sparse Randomized Embeddings | Paper Citation Record

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This paper cites Extensions of Lipschitz mappings into a Hilbert space,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Extensions of Lipschitz mappings into a Hilbert space,

Reference 1

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This paper cites Approximate nearest neighbors: Towards removing the curse of dimensionality,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Approximate nearest neighbors: Towards removing the curse of dimensionality,

Reference 2

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This paper cites Latent semantic indexing: A probabilistic analysis,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Latent semantic indexing: A probabilistic analysis,

Reference 3

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On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Unresolved cited work

Reference 4

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This paper cites Holographic reduced representations,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Holographic reduced representations,

Reference 5

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This paper cites Fully distributed representation,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Fully distributed representation,

Reference 6

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This paper cites A survey on hyperdimensional computing aka vector symbolic architectures, Part I: Models and data transformations,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A survey on hyperdimensional computing aka vector symbolic architectures, Part I: Models and data transformations,

Reference 7

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This paper cites Vector symbolic architectures answer Jackendoff’s chal- lenges for cognitive neuroscience,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Vector symbolic architectures answer Jackendoff’s chal- lenges for cognitive neuroscience,

Reference 8

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This paper cites A survey on hyperdimensional computing aka vector symbolic architectures, Part II: Applications, cognitive models, and challenges,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A survey on hyperdimensional computing aka vector symbolic architectures, Part II: Applications, cognitive models, and challenges,

Reference 9

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This paper cites A survey of dimensionality reduction techniques based on random projection.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A survey of dimensionality reduction techniques based on random projection

Reference 10

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This paper cites Database-friendly random proj.: Johnson-Lindenstrauss with binary coins,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Database-friendly random proj.: Johnson-Lindenstrauss with binary coins,

Reference 11

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This paper cites Very sparse random projections,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Very sparse random projections,

Reference 12

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This paper cites Sparser Johnson-Lindenstrauss transforms,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Sparser Johnson-Lindenstrauss transforms,

Reference 13

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This paper cites Random indexing of text samples for latent semantic analysis,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Random indexing of text samples for latent semantic analysis,

Reference 14

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This paper cites Vector-based semantic analysis: Representing word mean- ings based on random labels,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Vector-based semantic analysis: Representing word mean- ings based on random labels,

Reference 15

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This paper cites Vector and distributed representations reflecting semantic relatedness of words,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Vector and distributed representations reflecting semantic relatedness of words,

Reference 16

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This paper cites Similarity estimation techniques from rounding algo- rithms,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Similarity estimation techniques from rounding algo- rithms,

Reference 17

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This paper cites Searching for text information with the help of vector representations,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Searching for text information with the help of vector representations,

Reference 18

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This paper cites Formation of similarity-reflecting binary vectors with random binary projections,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Formation of similarity-reflecting binary vectors with random binary projections,

Reference 19

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This paper cites Randomized projective methods for the construction of binary sparse vector representations,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Randomized projective methods for the construction of binary sparse vector representations,

Reference 20

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This paper cites Estimation of vectors similarity by their randomized binary projections,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Estimation of vectors similarity by their randomized binary projections,

Reference 21

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This paper cites A neural algorithm for a fundamental computing problem,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A neural algorithm for a fundamental computing problem,

Reference 22

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This paper cites The Drosophila mushroom body: from architecture to algorithm in a learning circuit,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings The Drosophila mushroom body: from architecture to algorithm in a learning circuit,

Reference 23

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This paper cites Kanerva, Sparse distributed memory.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Kanerva, Sparse distributed memory

Reference 24

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This paper cites High-dimensional computing with sparse vectors,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings High-dimensional computing with sparse vectors,

Reference 25

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This paper cites Neural distributed autoassociative memories: A survey,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Neural distributed autoassociative memories: A survey,

Reference 26

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This paper cites Variable binding for sparse distributed representa- tions: Theory and applications,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Variable binding for sparse distributed representa- tions: Theory and applications,

Reference 27

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This paper cites Product quantization for nearest neighbor search,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Product quantization for nearest neighbor search,

Reference 28

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This paper cites GloVe: Global vectors for word representation,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings GloVe: Global vectors for word representation,

Reference 29

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On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Gradient-based learning applied to document recogni- tion,

Reference 30

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This paper cites Compressed hashing,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Compressed hashing,

Reference 31

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This paper cites Random convergence of olfactory inputs in the Drosophila mushroom body,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Random convergence of olfactory inputs in the Drosophila mushroom body,

Reference 32

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This paper cites Expansion of inf. in the binary autoencoder with random binary weights,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Expansion of inf. in the binary autoencoder with random binary weights,

Reference 33

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This paper cites Can a fruit fly learn word embeddings?.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Can a fruit fly learn word embeddings?

Reference 34

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This paper cites Attention approximates sparse distributed memory,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Attention approximates sparse distributed memory,

Reference 35

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This paper cites Reducing catastrophic forgetting with assoc. learning: A lesson from fruit flies,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Reducing catastrophic forgetting with assoc. learning: A lesson from fruit flies,

Reference 36

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This paper cites Discrete key-value bottleneck,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Discrete key-value bottleneck,

Reference 37

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This paper cites Sparse distributed memory is a continual learner,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Sparse distributed memory is a continual learner,

Reference 38

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On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Random features for large-scale kernel machines,

Reference 39

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This paper cites Computing on functions using randomized vector representations (in brief),.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Computing on functions using randomized vector representations (in brief),

Reference 40

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This paper cites Streaming Encoding Algorithms for Scalable Hyperdimensional Computing.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Streaming Encoding Algorithms for Scalable Hyperdimensional Computing

Reference 41

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This paper cites Bio-inspired hashing for unsupervised similarity search,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Bio-inspired hashing for unsupervised similarity search,

Reference 42

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This paper cites Laplace-HDC: Understanding the geometry of binary hyperdimensional computing.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Laplace-HDC: Understanding the geometry of binary hyperdimensional computing

Reference 43

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This paper cites Sparse neural networks with large learning diversity,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Sparse neural networks with large learning diversity,

Reference 44

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This paper cites Iter. retrieval and block coding in auto- and hetero-assoc. memory,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Iter. retrieval and block coding in auto- and hetero-assoc. memory,

Reference 45

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This paper cites Expressivity of expand-and-sparsify representations.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Expressivity of expand-and-sparsify representations

Reference 46

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This paper cites A theory of sequence indexing and working memory in recurrent neural networks,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A theory of sequence indexing and working memory in recurrent neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:16:08.300761Z

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Observation 4ea9d081-7ff2-416f-b198-22e543c2a972 · outbound

This paper cites A comparison of vector symbolic architectures,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A comparison of vector symbolic architectures,

Reference 48

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Observation 6f591be1-202f-4d0e-a0f5-b099ad32aa84 · outbound

This paper cites Near-channel classifier: Symbiotic communication and classification in high-dimensional space,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Near-channel classifier: Symbiotic communication and classification in high-dimensional space,

Reference 49

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This paper cites A theoretical perspective on hyperdimensional computing,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings A theoretical perspective on hyperdimensional computing,

Reference 50

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This paper cites Capacity Analysis of Vector Symbolic Architectures.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Capacity Analysis of Vector Symbolic Architectures

Reference 51

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This paper cites Efficient decoding of compositional structure in holistic representations,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Efficient decoding of compositional structure in holistic representations,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:16:08.244680Z

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This paper cites Hyperdimensional computing as a frame- work for systematic aggregation of image descriptors,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Hyperdimensional computing as a frame- work for systematic aggregation of image descriptors,

Reference 53

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This paper cites Modification of holographic graph neuron using sparse distributed representations,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Modification of holographic graph neuron using sparse distributed representations,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:16:08.216851Z

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This paper cites Classification using hyperdimensional comput- ing: A review,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings Classification using hyperdimensional comput- ing: A review,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:16:08.202647Z

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This paper cites UCI machine learning repository,.

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings UCI machine learning repository,

Reference 56

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