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Bruno A. Olshausen | Friedrich T. Sommer | Denis Kleyko | E. Paxon Frady | Christopher J. Kymn | B. Olshausen | F. Sommer | E. P. Frady | Chris Kymn | D. Kleyko
[1] Michael N Jones,et al. Representing word meaning and order information in a composite holographic lexicon. , 2007, Psychological review.
[2] Marco Marelli,et al. Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions , 2019, Perspectives on psychological science : a journal of the Association for Psychological Science.
[3] Terrence C. Stewart,et al. A neural representation of continuous space using fractional binding , 2019, CogSci.
[4] T. Hafting,et al. Microstructure of a spatial map in the entorhinal cortex , 2005, Nature.
[5] S. V. Slipchenko,et al. Randomized projective methods for the construction of binary sparse vector representations , 2012 .
[6] Ingrid K. Glad,et al. Correction of Density Estimators that are not Densities , 2003 .
[7] S. Frick,et al. Compressed Sensing , 2014, Computer Vision, A Reference Guide.
[8] Tony Plate,et al. Holographic Recurrent Networks , 1992, NIPS.
[9] Chris Eliasmith,et al. The third contender: A critical examination of the Dynamicist theory of cognition , 1996 .
[10] J. Dupuy,et al. Reproducing kernels based schemes for nonparametric regression , 2020, 2001.11213.
[11] T. Gelder,et al. Mind as Motion: Explorations in the Dynamics of Cognition , 1995 .
[12] Wei Ji Ma,et al. Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.
[13] Tony Plate,et al. Estimating Analogical Similarity by Dot-Products of Holographic Reduced Representations , 1993, NIPS.
[14] Pentti Kanerva,et al. Sparse Distributed Memory , 1988 .
[15] Bruno A. Olshausen,et al. Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures , 2020, Neural Computation.
[16] Trevor Cohen,et al. Reasoning with vectors: A continuous model for fast robust inference , 2015, Log. J. IGPL.
[17] Brent Komer,et al. Efficient navigation using a scalable, biologically inspired spatial representation , 2020, CogSci.
[18] F. Sommer,et al. A framework for linking computations and rhythm-based timing patterns in neural firing, such as phase precession in hippocampal place cells , 2018 .
[19] Estimation of a quadratic regression functional using the sinc kernel , 2007 .
[20] Zenon W. Pylyshyn,et al. Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.
[21] Tony A. Plate,et al. Holographic reduced representations , 1995, IEEE Trans. Neural Networks.
[22] C. J. van Rijsbergen,et al. The geometry of information retrieval , 2004 .
[23] Dmitri A. Rachkovskij,et al. Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning , 2001, Neural Computation.
[24] Jan M. Rabaey,et al. High-Dimensional Computing as a Nanoscalable Paradigm , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.
[25] Paul Thagard,et al. Concepts as Semantic Pointers: A Framework and Computational Model , 2016, Cogn. Sci..
[26] K. B. Davis. Mean Integrated Square Error Properties of Density Estimates , 1977 .
[27] J. Rabaey,et al. A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition , 2020, Nature Electronics.
[28] Dmitri A. Rachkovskij,et al. SIMILARITY‐BASED RETRIEVAL WITH STRUCTURE‐SENSITIVE SPARSE BINARY DISTRIBUTED REPRESENTATIONS , 2012, Comput. Intell..
[29] Michael Rabadi,et al. Kernel Methods for Machine Learning , 2015 .
[30] Friedrich T. Sommer,et al. Robust computation with rhythmic spike patterns , 2019, Proceedings of the National Academy of Sciences.
[31] Luca Benini,et al. Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals , 2019, Proceedings of the IEEE.
[32] Denis Kleyko,et al. Autoscaling Bloom filter: controlling trade-off between true and false positives , 2017, Neural Computing and Applications.
[33] H Barlow,et al. Redundancy reduction revisited , 2001, Network.
[34] Brent Komer,et al. Biologically Inspired Spatial Representation , 2020 .
[35] Jan M. Rabaey,et al. Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware , 2021, ArXiv.
[36] Aaron R. Voelker. A short letter on the dot product between rotated Fourier transforms , 2020, ArXiv.
[37] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[38] J. Goodman. Speckle Phenomena in Optics: Theory and Applications , 2020 .
[39] Lukas Gonon,et al. Discrete-Time Signatures and Randomness in Reservoir Computing , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[40] Tony Plate,et al. Holographic Reduced Representations: Convolution Algebra for Compositional Distributed Representations , 1991, IJCAI.
[41] Todorka Kovacheva,et al. LINEAR CLASSIFIERS BASED ON BINARY DISTRIBUTED REPRESENTATIONS , 2007 .
[42] Klaus Greff,et al. On the Binding Problem in Artificial Neural Networks , 2020, ArXiv.
[43] C. Eliasmith,et al. Accurate representation for spatial cognition using grid cells , 2020, CogSci.
[44] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[45] Chris Eliasmith,et al. Representing spatial relations with fractional binding , 2019, CogSci.
[46] R. Gilmore,et al. Lie Groups, Lie Algebras, and Some of Their Applications , 1974 .
[47] K. B. Davis,et al. Mean Square Error Properties of Density Estimates , 1975 .
[48] Kaspar Anton Schindler,et al. A Primer on Hyperdimensional Computing for iEEG Seizure Detection , 2021, Frontiers in Neurology.
[49] Friedrich T. Sommer,et al. When Can Dictionary Learning Uniquely Recover Sparse Data From Subsamples? , 2011, IEEE Transactions on Information Theory.
[50] Lewenstein,et al. Optimal storage of correlated patterns in neural-network memories. , 1992, Physical review. A, Atomic, molecular, and optical physics.
[51] Alexander Legalov,et al. Associative synthesis of finite state automata model of a controlled object with hyperdimensional computing , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.
[52] Stefano Fusi,et al. Why neurons mix: high dimensionality for higher cognition , 2016, Current Opinion in Neurobiology.
[53] Velio A. Marsocci. An Error Analysis of Electronic Analog Computers , 1956, IRE Trans. Electron. Comput..
[54] P. Gács,et al. Algorithms , 1992 .
[55] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[56] Emmanuel Dupoux,et al. Holographic String Encoding , 2011, Cogn. Sci..
[57] Peter Exterkate. Modelling Issues in Kernel Ridge Regression , 2011 .
[58] D. Smith,et al. A random walk in Hamming space , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[59] Michael N. Jones,et al. Encoding Sequential Information in Semantic Space Models: Comparing Holographic Reduced Representation and Random Permutation , 2015, Comput. Intell. Neurosci..
[60] M. Ledoux. The concentration of measure phenomenon , 2001 .
[61] G. Schöner,et al. Dynamic Field Theory of Movement Preparation , 2022 .
[62] Ross W. Gayler. Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience , 2004, ArXiv.
[63] Friedrich T. Sommer,et al. Variable Binding for Sparse Distributed Representations: Theory and Applications , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[64] Jan M. Rabaey,et al. Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[65] L. Devroye. A Note on the Usefulness of Superkernels in Density Estimation , 1992 .
[66] Jussi H. Poikonen,et al. High-dimensional computing with sparse vectors , 2015, 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS).
[67] Ross W. Gayler,et al. Multiplicative Binding, Representation Operators & Analogy , 1998 .
[68] Nikolaos Papakonstantinou,et al. Hyperdimensional Computing in Industrial Systems: The Use-Case of Distributed Fault Isolation in a Power Plant , 2018, IEEE Access.
[69] Graham Cormode,et al. An improved data stream summary: the count-min sketch and its applications , 2004, J. Algorithms.
[70] Sanjoy Dasgupta,et al. A neural algorithm for a fundamental computing problem , 2017 .
[71] Alexander V. Goltsev,et al. An assembly neural network for texture segmentation , 1996, Neural Networks.
[72] J. Walkup,et al. Statistical optics , 1986, IEEE Journal of Quantum Electronics.
[73] E.J. Candes. Compressive Sampling , 2022 .
[74] Ivan Tyukin,et al. Blessing of dimensionality: mathematical foundations of the statistical physics of data , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[75] Friedrich T. Sommer,et al. Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication , 2010, NIPS.
[76] Geoffrey E. Hinton,et al. Distributed representations and nested compositional structure , 1994 .
[77] H. Sompolinsky,et al. Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. , 2012, Annual review of neuroscience.
[78] Eric A. Weiss,et al. The Hyperdimensional Stack Machine , 2018 .
[79] Dmitri A. Rachkovskij,et al. Neural Distributed Autoassociative Memories: A Survey , 2017, ArXiv.
[80] Burton H. Bloom,et al. Space/time trade-offs in hash coding with allowable errors , 1970, CACM.
[81] Xin-She Yang,et al. Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.
[82] Dominic Widdows,et al. Geometry and Meaning , 2004, Computational Linguistics.
[83] Wenxing Ye,et al. A Geometric Construction of Multivariate Sinc Functions , 2012, IEEE Transactions on Image Processing.
[84] Pentti Kanerva,et al. Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors , 2009, Cognitive Computation.
[85] D. Field,et al. Natural image statistics and efficient coding. , 1996, Network.
[86] H. Sompolinsky,et al. Sparseness and Expansion in Sensory Representations , 2014, Neuron.
[87] Peter Blouw,et al. Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers , 2021, Neural Computation.
[88] C.E. Shannon,et al. Communication in the Presence of Noise , 1949, Proceedings of the IRE.
[89] Aditya Joshi,et al. Language Geometry Using Random Indexing , 2016, QI.
[90] Sridevi V. Sarma,et al. A Novel Nonparametric Maximum Likelihood Estimator for Probability Density Functions , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[91] G. Ermentrout,et al. Existence and uniqueness of travelling waves for a neural network , 1993, Proceedings of the Royal Society of Edinburgh: Section A Mathematics.
[92] Peer Neubert,et al. An Introduction to Hyperdimensional Computing for Robotics , 2019, KI - Künstliche Intelligenz.
[93] Andreas T. Schaefer,et al. Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. , 2003, Journal of neurophysiology.
[94] Pentti Kanerva,et al. Fully Distributed Representation , 1997 .
[95] S. Amari. Dynamics of pattern formation in lateral-inhibition type neural fields , 1977, Biological Cybernetics.
[96] Geoffrey E. Hinton. Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .
[97] Dmitri A. Rachkovskij,et al. Representation and Processing of Structures with Binary Sparse Distributed Codes , 2001, IEEE Trans. Knowl. Data Eng..
[98] Evgeny Osipov,et al. Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[99] Friedrich T. Sommer,et al. A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks , 2018, Neural Computation.
[100] R. V. Churchill,et al. Lectures on Fourier Integrals , 1959 .
[101] H. Haken,et al. Field Theory of Electromagnetic Brain Activity. , 1996, Physical review letters.
[102] W. Rudin,et al. Fourier Analysis on Groups. , 1965 .
[103] Pentti Kanerva,et al. Binary Spatter-Coding of Ordered K-Tuples , 1996, ICANN.
[104] Colin Raffel,et al. Thermometer Encoding: One Hot Way To Resist Adversarial Examples , 2018, ICLR.
[105] John W. Clark,et al. Neural Representation of Probabilistic Information , 2001, Neural Computation.
[106] S Edelman,et al. Representation is representation of similarities , 1996, Behavioral and Brain Sciences.
[107] M. Aizerman,et al. Theoretical foundation of potential functions method in pattern recognition , 2019 .
[108] Pedro M. Domingos,et al. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine , 2020, ArXiv.
[109] Okko Johannes Räsänen,et al. Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[110] Geoffrey E. Hinton,et al. Deep learning for AI , 2021, Commun. ACM.
[111] Javier Snaider,et al. Modular Composite Representation , 2014, Cognitive Computation.
[112] R. Potthast,et al. Inverse problems in dynamic cognitive modeling. , 2009, Chaos.
[113] 俊一 甘利. 5分で分かる!? 有名論文ナナメ読み:Jacot, Arthor, Gabriel, Franck and Hongler, Clement : Neural Tangent Kernel : Convergence and Generalization in Neural Networks , 2020 .
[114] Paul Thagard,et al. Integrating structure and meaning: a distributed model of analogical mapping , 2001 .