15 citations · 44 across the 13 of their papers we have counts for
15 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…
FlashMap: A Flash Optimized Key-Value Store
Zonglin Guo, Tony Givargis
Key-value stores are a fundamental class of NoSQL databases that offer a simple yet powerful model for data storage and retrieval, representing information as pairs of unique keys…
Advancing Intoxication Detection: A Smartwatch-Based Approach
Manuel Segura, Pere Vergés, Richard Ky +7
Excess alcohol consumption leads to serious health risks and severe consequences for both individuals and their communities. To advocate for healthier drinking habits, we introduce…
Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices
Manuel E. Segura, Pere Verges, Justin Tian Jin Chen +6
Alcohol consumption has a significant impact on individuals' health, with even more pronounced consequences when consumption becomes excessive. One approach to promoting healthier…
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…
Always-Sparse Training by Growing Connections with Guided Stochastic Exploration
Mike Heddes, Narayan Srinivasa, Tony Givargis +1
The excessive computational requirements of modern artificial neural networks (ANNs) are posing limitations on the machines that can run them. Sparsification of ANNs is often motiv…
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…
HDCC: A Hyperdimensional Computing compiler for classification on embedded systems and high-performance computing
Pere Vergés, Mike Heddes, Igor Nunes +2
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…
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…
OPEB: Open Physical Environment Benchmark for Artificial Intelligence
Hamid Mirzaei, Mona Fathollahi, Tony Givargis
Artificial Intelligence methods to solve continuous- control tasks have made significant progress in recent years. However, these algorithms have important limitations and still ne…
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
Hamid Mirzaei, Tony Givargis
Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging c…