A survey on hyperdimensional computing aka vector symbolic architectures, part i: Models and data transformations
This two-part comprehensive survey is devoted to a computing framework most commonly
known under the names Hyperdimensional Computing and Vector Symbolic Architectures …
known under the names Hyperdimensional Computing and Vector Symbolic Architectures …
A survey on hyperdimensional computing aka vector symbolic architectures, part ii: Applications, cognitive models, and challenges
This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
Explainable subgraph reasoning for forecasting on temporal knowledge graphs
Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest.
Here, graph representation learning has become the dominant paradigm for link prediction …
Here, graph representation learning has become the dominant paradigm for link prediction …
Graphd: Graph-based hyperdimensional memorization for brain-like cognitive learning
Memorization is an essential functionality that enables today's machine learning algorithms
to provide a high quality of learning and reasoning for each prediction. Memorization gives …
to provide a high quality of learning and reasoning for each prediction. Memorization gives …
Vector symbolic architectures as a computing framework for emerging hardware
This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …
Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion
There has recently been increasing interest in learning representations of temporal
knowledge graphs (KGs), which record the dynamic relationships between entities over …
knowledge graphs (KGs), which record the dynamic relationships between entities over …
Graph hawkes neural network for forecasting on temporal knowledge graphs
The Hawkes process has become a standard method for modeling self-exciting event
sequences with different event types. A recent work has generalized the Hawkes process to …
sequences with different event types. A recent work has generalized the Hawkes process to …
Relhd: A graph-based learning on fefet with hyperdimensional computing
Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …
relational data. GNNs are computationally demanding since they rely upon backpropagation …
A programmable hyper-dimensional processor architecture for human-centric IoT
S Datta, RAG Antonio, ARS Ison… - IEEE Journal on …, 2019 - ieeexplore.ieee.org
Hyper-dimensional Computing (HDC), a bio-inspired paradigm defined on random high-
dimensional vectors, has emerged as a promising IoT paradigm. It is known to provide …
dimensional vectors, has emerged as a promising IoT paradigm. It is known to provide …
xerte: Explainable reasoning on temporal knowledge graphs for forecasting future links
Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest.
Here, graph representation learning has become the dominant paradigm for link prediction …
Here, graph representation learning has become the dominant paradigm for link prediction …