4 citations · 8 across the 8 of their papers we have counts for
8 papers
Symplectic Representation of Legendre Dynamics
Robert Simon Fong, Gouhei Tanaka, Kazuyuki Aihara
Modern learning systems act on internal representations of data, yet how these representations encode underlying physical or statistical structure is often left implicit. In physic…
Structuring Multiple Simple Cycle Reservoirs with Particle Swarm Optimization
Ziqiang Li, Robert Simon Fong, Kantaro Fujiwara +2
Reservoir Computing (RC) is a time-efficient computational paradigm derived from Recurrent Neural Networks (RNNs). The Simple Cycle Reservoir (SCR) is an RC model that stands out f…
Linear Simple Cycle Reservoirs at the edge of stability perform Fourier decomposition of the input driving signals
Robert Simon Fong, Boyu Li, Peter Tino
This paper explores the representational structure of linear Simple Cycle Reservoirs (SCR) operating at the edge of stability. We view SCR as providing in their state space feature…
Universality of Real Minimal Complexity Reservoir
Robert Simon Fong, Boyu Li, Peter Tiňo
Reservoir Computing (RC) models, a subclass of recurrent neural networks, are distinguished by their fixed, non-trainable input layer and dynamically coupled reservoir, with only t…
Efficient Digital Quadratic Unconstrained Binary Optimization Solvers for SAT Problems
Robert Simon Fong, Yanming Song, Alexander Yosifov
Boolean satisfiability (SAT) is a propositional logic problem of determining whether an assignment of variables satisfies a Boolean formula. Many combinatorial optimization problem…
Predictive Modeling in the Reservoir Kernel Motif Space
Peter Tino, Robert Simon Fong, Roberto Fabio Leonarduzzi
This work proposes a time series prediction method based on the kernel view of linear reservoirs. In particular, the time series motifs of the reservoir kernel are used as represen…
Simple Cycle Reservoirs are Universal
Boyu Li, Robert Simon Fong, Peter Tiňo
Reservoir computation models form a subclass of recurrent neural networks with fixed non-trainable input and dynamic coupling weights. Only the static readout from the state space…
A Framework for Population-Based Stochastic Optimization on Abstract Riemannian Manifolds
Robert Simon Fong, Peter Tino
We present Extended Riemannian Stochastic Derivative-Free Optimization (Extended RSDFO), a novel population-based stochastic optimization algorithm on Riemannian manifolds that add…