This work proposes a flattening methodology that preserves GHRR's matrix-based encoding while executing training and inference directly in vector space, functionally equivalent to FHRR inference yet free of permutation logic.
William Youngwoo Chung, Hyunwoo Oh, Calvin Yeung et al.· International Symposium on L...· 0 citations
Neuro-symbolic models may improve robustness by combining learned representations with structured composition, but their behavior under adversarial perturbation remains underexplored. We study a hybrid pipeline that fuses ViT features with classical descriptors through Hyperdimensional Computing (HDC). Across CIFAR10 (...
Hamza Errahmouni Barkam, Salaar Saraj, Zhen Ye et al.· International Symposium on L...· 0 citations
This work introduces a state-of-the-art, out-of-order vector processor to natively accelerate VSA world modeling for autonomous systems with a power envelope of less than 130 mW, and unifies a structured, generalizable world model with hardware-efficient vector processing, which enables scalable and powerful autonomous...
Andrew Ding, William Youngwoo Chung, N. Bagherzadeh et al.· International Symposium on L...· 0 citations
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