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09/01/2023
Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds
Devising deep latent variable models for multi-modal data has been a lon...
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05/02/2023
Memory of recurrent networks: Do we compute it right?
Numerical evaluations of the memory capacity (MC) of recurrent neural ne...
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04/02/2023
Infinite-dimensional reservoir computing
Reservoir computing approximation and generalization bounds are proved f...
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02/21/2023
The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle
On Euclidean spaces, the Gaussian kernel is one of the most widely used ...
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12/30/2022
Reservoir kernels and Volterra series
A universal kernel is constructed whose sections approximate any causal ...
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09/16/2022
Transport in reservoir computing
Reservoir computing systems are constructed using a driven dynamical sys...
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08/11/2021
Learning strange attractors with reservoir systems
This paper shows that the celebrated Embedding Theorem of Takens is a pa...
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10/22/2020
Fading memory echo state networks are universal
Echo state networks (ESNs) have been recently proved to be universal app...
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09/17/2020
Discrete-time signatures and randomness in reservoir computing
A new explanation of geometric nature of the reservoir computing phenome...
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07/23/2020
Dimension reduction in recurrent networks by canonicalization
Many recurrent neural network machine learning paradigms can be formulat...
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04/22/2020
Memory and forecasting capacities of nonlinear recurrent networks
The notion of memory capacity, originally introduced for echo state and ...
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02/14/2020
Approximation Bounds for Random Neural Networks and Reservoir Systems
This work studies approximation based on single-hidden-layer feedforward...
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10/30/2019
Risk bounds for reservoir computing
We analyze the practices of reservoir computing in the framework of stat...
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02/16/2019
Differentiable reservoir computing
Much effort has been devoted in the last two decades to characterize the...
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07/07/2018
Reservoir Computing Universality With Stochastic Inputs
The universal approximation properties with respect to L ^p -type criter...
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06/03/2018
Echo state networks are universal
This paper shows that echo state networks are universal uniform approxim...
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12/03/2017
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
A new class of non-homogeneous state-affine systems is introduced. Suffi...
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05/29/2016
Singular ridge regression with homoscedastic residuals: generalization error with estimated parameters
This paper characterizes the conditional distribution properties of the ...
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10/13/2015
Nonlinear memory capacity of parallel time-delay reservoir computers in the processing of multidimensional signals
This paper addresses the reservoir design problem in the context of dela...
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08/01/2015
