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Found 7 papers, 5 papers with code
Molecular Classification Using Hyperdimensional Graph Classification
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing.
Always-Sparse Training by Growing Connections with Guided Stochastic Exploration
Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training.
DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication
DotHash can be used to estimate the Jaccard index and, to the best of our knowledge, is the first method that can also estimate the Adamic-Adar index and a family of related metrics.
HDCC: A Hyperdimensional Computing compiler for classification on embedded systems and high-performance computing
The experiments were run on four different machines, including different hyperparameter configurations, and the results were compared to a popular prototyping library built on PyTorch.
Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures
Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces.
GraphHD: Efficient graph classification using hyperdimensional computing
HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness.
An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing
Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces.
