Vector Symbolic Architectures

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Sep 23, 2026Mohamed Malek Abid, P. Michael FurlongVariational Autoencoders Visual Representation Learning
Institute of Neuroinformatics, University of Zurich & ETH Zurich · National Research Council Canada & Centre for Theoretical Neuroscience, Systems Design Engineering, University of Waterloo, Waterloo, Canada
Learning a Vector-Symbolic Model for Socio-Cultural Tasks
Aug 3, 2026Meera Ray, Swapnika Dulam, Christopher L. DancyComputational Cognitive Modeling Vector Symbolic Architectures
Department of Computer Science and Engineering, The Pennsylvania State University W209 Westgate Building, University Park, PA 16802. USA · Department of Industrial and Manufacturing Engineering, The Pennsylvania State University M310 Leonhard Building, University Park, PA 16802. USA
Gram-Space: Structure-Preserving Codebook Compression for Memory-Efficient Neuro-Symbolic AI
Aug 2, 2026Weilun Wang, Wantong LiMemory-Efficient Inference Vector Symbolic Architectures
University of California, Riverside
How to Build Marcus's Algebraic Mind: From Minsky's Emotion-Machine Viewpoint
Jul 18, 2026Hiroyuki Chuma, Kanji Otsuka, Yoichi SatoComputational Cognitive Modeling Vector Symbolic Architectures
Institute of Innovation Research, Hitotsubashi University (Professor Emeritus) · Meisei University (Professor Emeritus) · Shuhari System
How to Build Marcus's Algebraic Mind: From Thagard's Brain--Mind Viewpoint
Jul 18, 2026Hiroyuki Chuma, Kanji Otsuka, Yoichi SatoComputational Cognitive Modeling Hyperdimensional Computing
Institute of Innovation Research, Hitotsubashi University (Professor Emeritus) · Meisei University (Professor Emeritus) · Shuhari System
Creating Intelligence: A Computational Foundation for AGI
Jun 30, 2026Peter OvermannComputational Cognitive Modeling Hyperdimensional Computing
Recursive Binding on a Budget: Subspace Carving in Order-p Tensor Memories
Jun 9, 2026Travis Pence, Daisuke Yamada, Vikas SinghMemory-Efficient Inference Vector Symbolic Architectures
Department of Computer Science, University of Wisconsin-Madison, United States · Department of Biostatistics and Med. Info., University of Wisconsin-Madison, United States
Disentanglement with Holographic Reduced Representations
Jun 8, 2026Jhonny J. Velasquez Olivera, Christo K. Thomas, Walid SaadDisentangled Representation Learning Unsupervised Representation Learning
Virginia Tech · Worcester Polytechnic Institute
Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
May 29, 2026Karim Habashy, Chris EliasmithFlow Matching Representation Learning
Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada · Center For Theoretical Neuroscience, University of Waterloo, Waterloo, Canada · Department of Philosophy, University of Waterloo, Waterloo, Canada
How to Build Marcus's Algebraic Mind: Algebro-Deterministic Substrate over Galois Fields
May 20, 2026Hiroyuki Chuma, Kanji Otsuk, Yoichi SatoComputational Cognitive Modeling Hyperdimensional Computing
Institute of Innovation Research, Hitotsubashi University, Kunitachi, Tokyo 186-8603, Japan (Professor Emeritus) · Meisei University, Hino, Tokyo, Japan (Professor Emeritus) · Shuhari System, Tokyo, Japan
Sutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures
May 20, 2026Emma LeonhartLogical Reasoning Differentiable Programming
Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphs
Apr 21, 2026Sahil Rajesh DhayalkarVector Symbolic Architectures Neuro-Symbolic Reasoning
Arizona State University
SRMU: Relevance-Gated Updates for Streaming Hyperdimensional Memories
Apr 16, 2026Shay Snyder, Andrew Capodieci, David Gorsich +1Hyperdimensional Computing Vector Symbolic Architectures
George Mason University · George Mason University, USA · Neya Robotics +3
HyperSpace: A Generalized Framework for Spatial Encoding in Hyperdimensional Representations
Apr 16, 2026Shay Snyder, Andrew Capodieci, David Gorsich +1Hyperdimensional Computing Vector Symbolic Architectures
George Mason University · George Mason University, Fairfax, Virginia, USA · Neya Robotics +3

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Learning Holographic Reduced Representations with Clifford Variational Autoencoders

Mohamed Malek Abid, P. Michael Furlong

Institute of Neuroinformatics, University of Zurich & ETH Zurich · National Research Council Canada & Centre for Theoretical Neuroscience, Systems Design Engineering, University of Waterloo, Waterloo, Canada

Variational Autoencoders Visual Representation Learning Hyperdimensional Computing Vector Symbolic Architectures

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Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly generated atomic vector symbols and fractional power encodings of real-valued data. Embedding unstructured data remains an open question. We present \textit{Clifford-VAE}, a variational autoencoder that learns to project data onto a Clifford torus in arbitrary dimensions. Experiments using the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate that Clifford-VAE produces representations that are competitive with those produced by Gaussian and Hyperspherical VAEs for semi-supervised classification tasks while outperforming Gaussian and Hyperspherical counterparts in the VSA benchmark tests of self-binding and unbinding, role-filler recovery, and bundle capacity. Clifford-VAE provides a principled technique for grounding perceptual data into a symbolic reasoning framework, providing a new approach to a long-standing problem in the VSA literature.

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