hyperdimensional-computing

The paper proposes WaferHDC, an online open-world wafer defect discovery framework based on hyperdimensional computing that dynamically detects and groups recurring defect patterns without labels or retraining, outperforming the strongest online baseline by 13.3 p.p. in adjusted Rand index on the WM-811K dataset.

A commentary on Nick Cammarata's Goodfire essay 'Neural Networks Are Already Clean' exploring whether a sparse 'semantic skeleton' of bipolar neurons plus external VSA hypervector memory could enable continual learning with small frozen models, while noting obstacles like tangled fact-routing and coordinate-registry alignment.

Phase-HDC提出用梯度阈值取代Adam优化器的历史状态来训练超维分类器，在几乎不存储额外状态的情况下匹配Adam的精度，相比float32 Adam节省16-23倍存储，相比8位Adam节省4-6倍。

The paper introduces NysHD, a method that bridges hyperdimensional computing and kernel methods via the Nyström approximation, allowing any positive-semidefinite similarity function to be used as an HDC encoding. It demonstrates improved classification accuracy on graph and string datasets compared to existing HDC encoding methods.

This paper proposes a qubit-efficient quantum framework for hyperdimensional computing decomposition that reduces representation cost from O(D) to O(log D) qubits while preserving the quadratic search advantage, achieving up to 2000x fewer qubits.

This paper proposes using Hyperdimensional Computing, specifically Holographic Reduced Representations, to embed tabular data rows for structured querying, enabling interpretable similarity thresholds and zero-match detection, outperforming a baseline method on row retrieval tasks.

D2H-AD is a novel anomaly detection framework using Hyperdimensional Computing (HDC) that combines distance-based and density-aware encoding. It outperforms five baselines across multiple benchmarks, offering lightweight, interpretable, and efficient performance for edge AI and IoT.

Introduces 'tastebud-memory', an open-source agent memory system using hyperdimensional computing to create reversible, searchable project fingerprints, with backtested validation and an MCP server for integration.

This paper proposes FedQHD, a novel federated Q-learning method using hyperdimensional random-feature state encoders with linear readouts to enable closed-form function-space aggregation, addressing the federation gap due to heterogeneous client encoders.