Denis Kleyko | DeepAI

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∙ 05/26/2023

Efficient Decoding of Compositional Structure in Holistic Representations

We investigate the task of retrieving information from compositional dis...

0 Denis Kleyko, et al. ∙

∙ 12/07/2022

Efficient Optimization with Higher-Order Ising Machines

A prominent approach to solving combinatorial optimization problems on p...

0 Connor Bybee, et al. ∙

∙ 08/28/2022

Computing with Hypervectors for Efficient Speaker Identification

We introduce a method to identify speakers by computing with high-dimens...

0 Ping-Chen Huang, et al. ∙

∙ 03/11/2022

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

Memory-augmented neural networks enhance a neural network with an extern...

0 Denis Kleyko, et al. ∙

∙ 03/02/2022

Integer Factorization with Compositional Distributed Representations

In this paper, we present an approach to integer factorization using dis...

0 Denis Kleyko, et al. ∙

∙ 01/27/2022

Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences

Hyperdimensional computing (HDC), also known as vector symbolic architec...

0 Dmitri A. Rachkovskij, et al. ∙

∙ 11/11/2021

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations

This two-part comprehensive survey is devoted to a computing framework m...

0 Denis Kleyko, et al. ∙

∙ 10/15/2021

HyperSeed: Unsupervised Learning with Vector Symbolic Architectures

Motivated by recent innovations in biologically-inspired neuromorphic ha...

0 Evgeny Osipov, et al. ∙

∙ 09/08/2021

Computing on Functions Using Randomized Vector Representations

Vector space models for symbolic processing that encode symbols by rando...

31 E. Paxon Frady, et al. ∙

∙ 06/17/2021

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

A change of the prevalent supervised learning techniques is foreseeable ...

0 Antonello Rosato, et al. ∙

∙ 06/17/2021

Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing

Machine learning algorithms deployed on edge devices must meet certain r...

0 Cameron Diao, et al. ∙

∙ 06/09/2021

Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware

This article reviews recent progress in the development of the computing...

0 Denis Kleyko, et al. ∙

∙ 06/02/2021

Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks

In the supervised learning domain, considering the recent prevalence of ...

0 Antonello Rosato, et al. ∙

∙ 12/14/2020

Perceptron Theory for Predicting the Accuracy of Neural Networks

Many neural network models have been successful at classification proble...

0 Denis Kleyko, et al. ∙

∙ 10/11/2020

End to End Binarized Neural Networks for Text Classification

Deep neural networks have demonstrated their superior performance in alm...

0 Harshil Jain, et al. ∙

∙ 10/07/2020

Cellular Automata Can Reduce Memory Requirements of Collective-State Computing

Various non-classical approaches of distributed information processing, ...

0 Denis Kleyko, et al. ∙

∙ 09/14/2020

Variable Binding for Sparse Distributed Representations: Theory and Applications

Symbolic reasoning and neural networks are often considered incompatible...

0 E. Paxon Frady, et al. ∙

∙ 03/25/2020

Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10

This correspondence comments on the findings reported in a recent Scienc...

0 Denis Kleyko, et al. ∙

∙ 03/03/2020

HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics

Recent advances in Deep Learning have led to a significant performance i...

0 Pedro Alonso, et al. ∙

∙ 09/19/2019

Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks

The deployment of machine learning algorithms on resource-constrained ed...

0 Denis Kleyko, et al. ∙

∙ 02/28/2018

A theory of sequence indexing and working memory in recurrent neural networks

To accommodate structured approaches of neural computation, we propose a...

0 E. Paxon Frady, et al. ∙

∙ 07/05/2017

Theory of the superposition principle for randomized connectionist representations in neural networks

To understand cognitive reasoning in the brain, it has been proposed tha...

0 E. Paxon Frady, et al. ∙

∙ 06/01/2017

Integer Echo State Networks: Hyperdimensional Reservoir Computing

We propose an integer approximation of Echo State Networks (ESN) based o...

0 Denis Kleyko, et al. ∙

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