Learning Holographic Reduced Representations with Clifford Variational Autoencoders

arXiv:2609.28409 · cs.LG, cs.AI, cs.NE · Submitted 2026-09-23 · Read on arXiv

cs.LG, cs.AI, cs.NE

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Accuracy — 47× in this paper · explained in 1 episode
• Benchmark — 28× in this paper · explained in 2 episodes
• Cosine Similarity — 20× in this paper · explained in 3 episodes
• Embeddings — 12× in this paper · explained in 2 episodes
• Latent Representation — 7× in this paper · explained in 2 episodes
• Latent Representations — 4× in this paper · explained in 3 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Vector Representations — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Fingerprinting — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Fourier Holographic Reduced Representation — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Recasting Self-Attention with Holographic Reduced Representations
• Computing on Functions Using Randomized Vector Representations
• Audio Fingerprinting with Holographic Reduced Representations
• Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience
• On the Binding Problem in Artificial Neural Networks
• On PyTorch Implementation of Density Estimators for von Mises-Fisher and Its Mixture
• Semi-Supervised Learning with Deep Generative Models
• Holographic Embeddings of Knowledge Graphs
• Spherical Cauchy Variational Autoencoders: Heavy Angular Tails and Exact KL Evaluation
• A comparison of Vector Symbolic Architectures

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