SHiPPO: Recurrent Memory with Transported Polynomial Projections | Paper Citation Record

Observation 761a3f31-1640-4785-9fd6-646eb151072f · outbound

This paper cites On the expressiveness of state space models via temporal logics, 2026.

SHiPPO: Recurrent Memory with Transported Polynomial Projections On the expressiveness of state space models via temporal logics, 2026

Reference 1

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Observation 7eecb0ad-a4b0-47e3-959e-e367418aca63 · outbound

This paper cites Zoology: Measuring and improving recall in efficient language models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Zoology: Measuring and improving recall in efficient language models

Reference 2

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Observation 0d177827-ddfc-4162-ba35-34cf320a50d2 · outbound

This paper cites xL- STM: Extended long short-term memory.

SHiPPO: Recurrent Memory with Transported Polynomial Projections xL- STM: Extended long short-term memory

Reference 3

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Observation 64a9387e-09db-4499-8445-48c1e3d8b95f · outbound

This paper cites Solution formulas for differential sylvester and lyapunov equations.Calcolo, 56:51, 2019.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Solution formulas for differential sylvester and lyapunov equations.Calcolo, 56:51, 2019

Reference 4

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This paper cites Blelloch.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Blelloch

Reference 5

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 6

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 7

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Observation 3ccda109-866a-41ba-b354-5adb13c4ce98 · outbound

This paper cites Learning phrase representations using RNN encoder– decoder for statistical machine translation.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Learning phrase representations using RNN encoder– decoder for statistical machine translation

Reference 8

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 9

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Observation c1cf21d4-9c10-4fb6-af79-0c73d327cb4a · outbound

This paper cites Coddington and Norman Levinson.Theory of Ordinary Differential Equations.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Coddington and Norman Levinson.Theory of Ordinary Differential Equations

Reference 10

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Observation 7d1a629a-2824-4331-aab9-aa736d1e2211 · outbound

This paper cites Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality

Reference 11

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Observation 583b6ffe-4c77-49ca-9338-1547cfb04877 · outbound

This paper cites Reza Ebrahimi and Roland Memisevic.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Reza Ebrahimi and Roland Memisevic

Reference 12

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Observation 06dc5eb3-bc8d-498b-9907-524785153ec8 · outbound

This paper cites Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, and Michael W.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, and Michael W

Reference 13

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Observation bef977a2-df61-4987-b8fd-906d8f3f1606 · outbound

This paper cites Priors in bayesian deep learning: A review.International Statistical Review, 90(3):563–591, 2022.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Priors in bayesian deep learning: A review.International Statistical Review, 90(3):563–591, 2022

Reference 14

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Observation 843d51be-47a4-4636-ad9f-b6d144ea99a3 · outbound

This paper cites Fu, Tri Dao, Khaled K.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Fu, Tri Dao, Khaled K

Reference 15

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Observation 600182a4-c8f3-418f-a637-c546dded38fe · outbound

This paper cites HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models

Reference 16

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Observation 5aa2cc53-d5c8-44b9-8280-f260d0503863 · outbound

This paper cites Gordon and Marie desJardins.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Gordon and Marie desJardins

Reference 17

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Observation b45ef6ac-ad52-4531-9cc0-09c8f46c3d6f · outbound

This paper cites Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues

Reference 18

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Observation 6861f93e-ebfd-40bf-bee4-5f1e1683c3d2 · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Mamba: Linear-time sequence modeling with selective state spaces

Reference 19

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Observation 7e2c8cae-05c1-4612-9c2d-e6057f1d46bc · outbound

This paper cites HiPPO: Recurrent memory with optimal polynomial projections.

SHiPPO: Recurrent Memory with Transported Polynomial Projections HiPPO: Recurrent memory with optimal polynomial projections

Reference 20

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Observation c3d399ee-3e3c-4735-a456-bf85e54c14de · outbound

This paper cites Efficiently modeling long sequences with struc- tured state spaces.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Efficiently modeling long sequences with struc- tured state spaces

Reference 21

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Observation f9693602-df51-46b3-843c-d1a62a1541e1 · outbound

This paper cites On the parameterization and initialization of diagonal state space models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections On the parameterization and initialization of diagonal state space models

Reference 22

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Observation 7eaecadf-4c08-4720-8127-d32e5dcd2a4b · outbound

This paper cites How to train your HiPPO: State space models with generalized orthogonal basis projections.

SHiPPO: Recurrent Memory with Transported Polynomial Projections How to train your HiPPO: State space models with generalized orthogonal basis projections

Reference 23

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Observation cecb10d5-c58d-4dea-81f8-7c687447b6c7 · outbound

This paper cites Diagonal state spaces are as effective as struc- tured state spaces.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Diagonal state spaces are as effective as struc- tured state spaces

Reference 24

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Observation 3cd0878c-80b6-44d0-88cf-4a2d0c29955f · outbound

This paper cites Springer, 2 edition,.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Springer, 2 edition,

Reference 25

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 26

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Observation 71c63f5e-85c8-41d8-9741-cb5dfea9f0b6 · outbound

This paper cites Liquid time-constant networks.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Liquid time-constant networks

Reference 27

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Observation 00bcb462-bfda-4bf2-b54f-6b64b627674d · outbound

This paper cites Liquid structural state-space models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Liquid structural state-space models

Reference 28

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Observation ea54c7f8-ac31-4dd1-9165-0aad3f8f16ac · outbound

This paper cites Higham.Functions of Matrices: Theory and Computation.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Higham.Functions of Matrices: Theory and Computation

Reference 29

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Observation b6249199-559e-4a35-bd01-8c1a2da790cc · outbound

This paper cites Understanding input selectivity in mamba: Impact on approximation power, memorization, and associative recall capacity, 2025.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Understanding input selectivity in mamba: Impact on approximation power, memorization, and associative recall capacity, 2025

Reference 30

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Observation e4dc86b0-b7b4-4823-ad14-a7a4beaae398 · outbound

This paper cites Anderson Keller, Carmen Amo Alonso, Terrence J.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Anderson Keller, Carmen Amo Alonso, Terrence J

Reference 31

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Observation 2b172d8a-42dc-491d-a0c2-7cf140e49ffc · outbound

This paper cites Neural controlled differen- tial equations for irregular time series.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Neural controlled differen- tial equations for irregular time series

Reference 32

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Observation e03c1908-7587-4fbe-ba75-4b9a4d46c8ca · outbound

This paper cites Kimi Linear: An Expressive, Efficient Attention Architecture.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Kimi Linear: An Expressive, Efficient Attention Architecture

Reference 33

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Observation 172a6c1a-f1bd-413d-a7e0-bd12beae2cbd · outbound

This paper cites Li, Berlin Chen, Caitlin Wang, Aviv Bick, J.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Li, Berlin Chen, Caitlin Wang, Aviv Bick, J

Reference 34

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Observation f431b68a-1110-4ceb-b3f3-2f39862ff5d2 · outbound

This paper cites UnHiPPO: Uncertainty-aware initialization for state space models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections UnHiPPO: Uncertainty-aware initialization for state space models

Reference 35

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This paper cites Longhorn: State space models are amortized online learners.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Longhorn: State space models are amortized online learners

Reference 36

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This paper cites Autocorrelation matters: Understanding the role of initialization schemes for state space models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Autocorrelation matters: Understanding the role of initialization schemes for state space models

Reference 37

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 38

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Observation 550ba071-a1e6-43f7-b032-4e2e1e3441cf · outbound

This paper cites Decoupled weight decay regularization.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Decoupled weight decay regularization

Reference 39

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Observation c0e4ad3c-207f-46be-9c49-d031a2d6e4ea · outbound

This paper cites Parallelizing linear recurrent neural nets over sequence length.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Parallelizing linear recurrent neural nets over sequence length

Reference 40

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This paper cites The illusion of state in state-space models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections The illusion of state in state-space models

Reference 41

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Observation 23c6cfb0-ccb3-4ed2-b03c-4360f978eb42 · outbound

This paper cites M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling.

SHiPPO: Recurrent Memory with Transported Polynomial Projections M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling

Reference 42

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Mitchell

Reference 43

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Observation bb89abf7-8243-4d78-aaec-bef4e7db4c57 · outbound

This paper cites Fixed-point RNNs: Interpolating from diagonal to dense, 2025.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Fixed-point RNNs: Interpolating from diagonal to dense, 2025

Reference 44

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 45

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This paper cites Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Raz- van Pascanu, and Soham De.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Raz- van Pascanu, and Soham De

Reference 46

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Observation 7da1b91b-3c40-44f2-bae6-52e7fc0a38ec · outbound

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SHiPPO: Recurrent Memory with Transported Polynomial Projections HGRN2: Gated linear RNNs with state expansion

Reference 47

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 48

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 49

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Observation af1c4ce8-4e72-450f-beb0-4995d29047b3 · outbound

This paper cites Konstantin Rusch and Siddhartha Mishra.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Konstantin Rusch and Siddhartha Mishra

Reference 50

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Konstantin Rusch and Daniela Rus

Reference 51

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This paper cites The expressive capacity of state space models: A formal language perspective.

SHiPPO: Recurrent Memory with Transported Polynomial Projections The expressive capacity of state space models: A formal language perspective

Reference 52

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This paper cites Linear transformers are secretly fast weight programmers.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Linear transformers are secretly fast weight programmers

Reference 53

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This paper cites The ex- pressive limits of diagonal SSMs for state-tracking.

SHiPPO: Recurrent Memory with Transported Polynomial Projections The ex- pressive limits of diagonal SSMs for state-tracking

Reference 54

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 55

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Observation 187c3cfd-a44d-494f-a94b-20c5b732659a · outbound

This paper cites Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks

Reference 56

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Observation 18d9635d-83a1-4352-a6e1-de964604419f · outbound

This paper cites Design Principles for Sequence Models via Coefficient Dynamics.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Design Principles for Sequence Models via Coefficient Dynamics

Reference 57

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Observation da8e5585-c2df-4936-ad78-b018b71b1c1e · outbound

This paper cites Deltaproduct: Increasing the expressivity of deltanet through products of householders.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Deltaproduct: Increasing the expressivity of deltanet through products of householders

Reference 58

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Observation 826bbd46-4b0a-4b6c-a084-30641116a5f2 · outbound

This paper cites Computational methods for linear matrix equations.SIAM Review, 58(3): 377–441, 2016.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Computational methods for linear matrix equations.SIAM Review, 58(3): 377–441, 2016

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Observation 7213c1d1-433e-412a-97ff-16219f9cfd0f · outbound

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SHiPPO: Recurrent Memory with Transported Polynomial Projections Unresolved cited work

Reference 60

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Observation 7fbab3bb-395a-43b8-919b-f809dd1297f3 · outbound

This paper cites Uncovering the Spectral Bias in Diagonal State Space Models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Uncovering the Spectral Bias in Diagonal State Space Models

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This paper cites Learning to (learn at test time): RNNs with expressive hidden states.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Learning to (learn at test time): RNNs with expressive hidden states

Reference 62

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Observation 2f204e16-b487-4e46-990e-35dddff78b51 · outbound

This paper cites On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages.

SHiPPO: Recurrent Memory with Transported Polynomial Projections On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages

Reference 63

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Observation 3d7fccb7-ea50-4121-bc2a-83f1657629d2 · outbound

This paper cites Structured sparse transition matrices to enable state tracking in state-space models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Structured sparse transition matrices to enable state tracking in state-space models

Reference 64

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Observation e1f6603d-1c03-4ca6-9b0d-f140ffaabc44 · outbound

This paper cites Mimetic Initialization Helps State Space Models Learn to Recall.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Mimetic Initialization Helps State Space Models Learn to Recall

Reference 65

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Observation 08af67b9-8fc6-41bb-8552-4eeb32569f91 · outbound

This paper cites On the implicit bias in deep-learning algorithms.Communications of the ACM, 66 (6):86–93, 2023.

SHiPPO: Recurrent Memory with Transported Polynomial Projections On the implicit bias in deep-learning algorithms.Communications of the ACM, 66 (6):86–93, 2023

Reference 66

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Observation 4bfc3a9e-662a-47f6-a39c-53c5a1398d4c · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 67

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Observation 4edf28f3-0779-4334-a594-3cb7af6439d3 · outbound

This paper cites V oelker, Ivana Kaji´c, and Chris Eliasmith.

SHiPPO: Recurrent Memory with Transported Polynomial Projections V oelker, Ivana Kaji´c, and Chris Eliasmith

Reference 68

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Observation 300bfcb0-b089-44a4-b6b5-b3d3661e58d6 · outbound

This paper cites Saurous, Charlotte Frenkel, Razvan Pascanu, Blaise Aguera y Arcas, and Joao Sacramento.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Saurous, Charlotte Frenkel, Razvan Pascanu, Blaise Aguera y Arcas, and Joao Sacramento

Reference 69

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Observation 5edbbc99-fbad-4da9-9152-639ee0c978bb · outbound

This paper cites Informed ma- chine learning—a taxonomy and survey of integrating prior knowledge into learning sys- tems.IEEE Transactions on Knowledge and Data Engineering, 35(1):614–633, 2023.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Informed ma- chine learning—a taxonomy and survey of integrating prior knowledge into learning sys- tems.IEEE Transactions on Knowledge and Data Engineering, 35(1):614–633, 2023

Reference 70

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Observation df7d6f7b-a1c7-49af-81fd-6a665b424d80 · outbound

This paper cites Struc- tured linear CDEs: Maximally expressive and parallel-in-time sequence models.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Struc- tured linear CDEs: Maximally expressive and parallel-in-time sequence models

Reference 71

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Observation 72096894-77ce-45f2-8ee4-3665b960f1a2 · outbound

This paper cites StableSSM: Alleviating the curse of memory in state-space mod- els through stable reparameterization.

SHiPPO: Recurrent Memory with Transported Polynomial Projections StableSSM: Alleviating the curse of memory in state-space mod- els through stable reparameterization

Reference 72

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Observation c9f1aa38-91a5-48b9-abbc-4c857c5fe602 · outbound

This paper cites State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory.

SHiPPO: Recurrent Memory with Transported Polynomial Projections State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory

Reference 73

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Observation 1892fa37-f35e-4403-8e24-f4ca382b2f61 · outbound

This paper cites Gated linear attention transformers with hardware-efficient training.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Gated linear attention transformers with hardware-efficient training

Reference 74

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Observation cafd08b3-7812-4c66-9c85-94772c71cf36 · outbound

This paper cites Parallelizing linear transformers with the delta rule over sequence length.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Parallelizing linear transformers with the delta rule over sequence length

Reference 75

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Observation 8593bb52-6bcf-494b-a48e-e52a4ba53699 · outbound

This paper cites Gated delta networks: Improving mamba2 with delta rule.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Gated delta networks: Improving mamba2 with delta rule

Reference 76

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Observation a587a9bb-0e40-4558-9b6a-19de1471ae01 · outbound

This paper cites Mahoney, and N.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Mahoney, and N

Reference 77

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Observation 57f4ca12-e71b-4e66-abf5-5a9951792bbc · outbound

This paper cites Mahoney, and N.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Mahoney, and N

Reference 78

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Observation 193678d2-f786-4a7f-8c1f-e63f4ec7fee5 · outbound

This paper cites Mahoney, and N.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Mahoney, and N

Reference 79

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Observation 11a1de15-32c8-44c4-9c1f-e2250df25b13 · outbound

This paper cites Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation

Reference 80

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Observation 7b6fd412-5562-43f9-9467-da0777e894c9 · outbound

This paper cites Thus the method is locally first-order accurate in∆ t.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Thus the method is locally first-order accurate in∆ t

Reference 81

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Observation 16fbf7f4-2c66-49c4-a3fe-746b8768e7ff · outbound

This paper cites Thus the method is locally second-order accurate in∆ t.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Thus the method is locally second-order accurate in∆ t

Reference 82

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Observation cb0bff6a-f106-482e-a09c-e8e19950f5e1 · outbound

This paper cites Eval batches.

SHiPPO: Recurrent Memory with Transported Polynomial Projections Eval batches

Reference 83

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