15 citations · 43 across the 11 of their papers we have counts for
12 papers
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck
Shubham Agarwal, Akshat Gupta, Mike Heddes +21
Evaluation has been central to advances in AI, and it is equally critical to autonomous discovery. Yet scaling autonomous discovery to real-world engineering challenges faces a fun…
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…
DeepCrossAttention: Supercharging Transformer Residual Connections
Mike Heddes, Adel Javanmard, Kyriakos Axiotis +3
Transformer networks have achieved remarkable success across diverse domains, leveraging a variety of architectural innovations, including residual connections. However, traditiona…
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…