15 citations · 35 across the 10 of their papers we have counts for
10 papers
Approximating Tensor Network Contraction with Sketches
Mike Heddes, Igor Nunes, Tony Givargis +1
Tensor network contraction is a fundamental mathematical operation that generalizes the dot product and matrix multiplication. It finds applications in numerous domains, such as da…
Molecular Classification Using Hyperdimensional Graph Classification
Pere Verges, Igor Nunes, Mike Heddes +2
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…
Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join Queries
Mike Heddes, Igor Nunes, Tony Givargis +1
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 algorit…
DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication
Igor Nunes, Mike Heddes, Pere Vergés +4
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 be…
A transient equivalence between Aldous-Broder and Wilson's algorithms and a two-stage framework for generating uniform spanning trees
Igor Nunes, Giulio Iacobelli, Daniel Ratton Figueiredo
The and are two well-known algorithms to generate uniform spanning trees (USTs) based on random walks. This work studies their relationship while th…
An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing
Igor Nunes, Mike Heddes, Tony Givargis +1
Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constr…
Hyperdimensional Hashing: A Robust and Efficient Dynamic Hash Table
Mike Heddes, Igor Nunes, Tony Givargis +2
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…
GraphHD: Efficient graph classification using hyperdimensional computing
Igor Nunes, Mike Heddes, Tony Givargis +2
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural syst…
Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures
Mike Heddes, Igor Nunes, Pere Vergés +5
Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random…
A Contextual Hierarchical Graph Model for Generating Random Sequences of Objects with Application to Music Playlists
Igor de Oliveira Nunes, Gabriel Matos Cardoso Leite, Daniel Ratton Figueiredo
Recommending the right content in large scale multimedia streaming services is an important and challenging problem that has received much attention in the past decade. A key ingre…