147 citations · 702 across the 33 of their papers we have counts for
6 papers · 1 filter
Perceptron Theory Can Predict the Accuracy of Neural Networks
Denis Kleyko, Antonello Rosato, E. Paxon Frady +2
Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzin…
End to End Binarized Neural Networks for Text Classification
Harshil Jain, Akshat Agarwal, Kumar Shridhar +1
Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns. In particu…
Cellular Automata Can Reduce Memory Requirements of Collective-State Computing
Denis Kleyko, E. Paxon Frady, Friedrich T. Sommer
Various non-classical approaches of distributed information processing, such as neural networks, computation with Ising models, reservoir computing, vector symbolic architectures,…
Variable Binding for Sparse Distributed Representations: Theory and Applications
E. Paxon Frady, Denis Kleyko, Friedrich T. Sommer
Symbolic reasoning and neural networks are often considered incompatible approaches. Connectionist models known as Vector Symbolic Architectures (VSAs) can potentially bridge this…
Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10
Denis Kleyko, Ross W. Gayler, Evgeny Osipov
This correspondence comments on the findings reported in a recent Science Robotics article by Mitrokhin et al. [1]. The main goal of this commentary is to expand on some of the iss…
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics
Pedro Alonso, Kumar Shridhar, Denis Kleyko +2
Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore,…