Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Nunes Igor"'
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is not
Externí odkaz:
http://arxiv.org/abs/2403.12307
With the increasing rate of data generated by critical systems, estimating functions on streaming data has become essential. This demand has driven numerous advancements in algorithms designed to efficiently query and analyze one or more data streams
Externí odkaz:
http://arxiv.org/abs/2402.15953
Autor:
Nunes, Igor, Heddes, Mike, Vergés, Pere, Abraham, Danny, Veidenbaum, Alexander, Nicolau, Alexandru, Givargis, Tony
Metrics for set similarity are a core aspect of several data mining tasks. To remove duplicate results in a Web search, for example, a common approach looks at the Jaccard index between all pairs of pages. In social network analysis, a much-celebrate
Externí odkaz:
http://arxiv.org/abs/2305.17310
Hyperdimensional Computing (HDC) is a bio-inspired computing framework that has gained increasing attention, especially as a more efficient approach to machine learning (ML). This work introduces the \name{} compiler, the first open-source compiler t
Externí odkaz:
http://arxiv.org/abs/2304.12398
The $Aldous\text{-}Broder$ and $Wilson$ are two well-known algorithms to generate uniform spanning trees (USTs) based on random walks. This work studies their relationship while they construct random trees with the goal of reducing the total time req
Externí odkaz:
http://arxiv.org/abs/2206.12378
Autor:
Heddes, Mike, Nunes, Igor, Vergés, Pere, Kleyko, Denis, Abraham, Danny, Givargis, Tony, Nicolau, Alexandru, Veidenbaum, Alexander
Publikováno v:
Journal of Machine Learning Research 24 (2023) 1--10
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. The commitment of the scientific comm
Externí odkaz:
http://arxiv.org/abs/2205.09208
Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such as embedded systems and IoT, as it achieves a
Externí odkaz:
http://arxiv.org/abs/2205.07920
Most cloud services and distributed applications rely on hashing algorithms that allow dynamic scaling of a robust and efficient hash table. Examples include AWS, Google Cloud and BitTorrent. Consistent and rendezvous hashing are algorithms that mini
Externí odkaz:
http://arxiv.org/abs/2205.07850
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic represen
Externí odkaz:
http://arxiv.org/abs/2205.07826
Publikováno v:
In Discrete Mathematics February 2025 348(2)
