147 citations · 702 across the 33 of their papers we have counts for
6 papers · 1 filter
Efficient Optimization with Higher-Order Ising Machines
Connor Bybee, Denis Kleyko, Dmitri E. Nikonov +3
A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin va…
Computing with Hypervectors for Efficient Speaker Identification
Ping-Chen Huang, Denis Kleyko, Jan M. Rabaey +2
We introduce a method to identify speakers by computing with high-dimensional random vectors. Its strengths are simplicity and speed. With only 1.02k active parameters and a 128-mi…
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…
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks
Denis Kleyko, Geethan Karunaratne, Jan M. Rabaey +2
Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memo…
Integer Factorization with Compositional Distributed Representations
Denis Kleyko, Connor Bybee, Christopher J. Kymn +5
In this paper, we present an approach to integer factorization using distributed representations formed with Vector Symbolic Architectures. The approach formulates integer factoriz…
Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences
Dmitri A. Rachkovskij, Denis Kleyko
Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that opera…