Vertical Slice Architecture solution template in .NET 10
Torchhd is a Python library for Hyperdimensional Computing and Vector Symbolic Architectures
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication betwee…
The Fuzzy-Pattern Tsetlin Machine library, with zero external dependencies, performs blazingly fast.
Rohde & Schwarz SCPI Driver (in Python)
Holographic Reduced Representations
"Commit information, not traffic" - Vector–Scalar Accumulator (VSA) is a lightweight algorithmic primitive that eliminates write amplification and hot-row contention in high-frequency systems
Domain-Agnostic Supervised Learning with Hyperdimensional Computing
Thesis project on the development of a framework for Value-Set Analysis
The holographic core for leOS
Holographic vectors you can compute with. Bind structure, bundle sets, unbind components cross NumPy, PyTorch, and JAX.
Sparse binary hypervectors and associated learners.
Video streaming web application [VSA], through HLS, using AES-128 encryption
A high-performance autonomous bitwise model leveraging Vector Symbolic Architectures (VSA) and Hyperdimensional Computing (HDC). Built in Rust.
An extended note on using Vector Symbolic Architectures to implement Next Generation Reservoir Computing
a basic VSA sample in c#
Volume Spread Analysis (VSA) is a powerful trading methodology that examines the relationship between volume, price spread (range), and closing price to identify market reversals, continuations, and potential manipulation by "smart money."
To associate your repository with the vsa topic, visit your repo's landing page and select "manage topics."