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Echo State Networks as State-Space Models: A Systems Perspective
Pradeep Singh, Balasubramanian Raman
Echo State Networks (ESNs) are typically presented as efficient, readout-trained recurrent models, yet their dynamics and design are often guided by heuristics rather than first pr…
HypER: Hyperbolic Echo State Networks for Capturing Stretch-and-Fold Dynamics in Chaotic Flows
Pradeep Singh, Sutirtha Ghosh, Ashutosh Kumar +2
Forecasting chaotic dynamics beyond a few Lyapunov times is difficult because infinitesimal errors grow exponentially. Existing Echo State Networks (ESNs) mitigate this growth but…
Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics
Pradeep Singh, Mehak Sharma, Anupriya Dey +1
Transformers are the de-facto choice for sequence modelling, yet their quadratic self-attention and weak temporal bias can make long-range forecasting both expensive and brittle. W…
Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective
Pradeep Singh, Ashutosh Kumar, Sutirtha Ghosh +2
Reservoir computing (RC) represents a class of state-space models (SSMs) characterized by a fixed state transition mechanism (the reservoir) and a flexible readout layer that maps…
SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix
Mrinmay Sen, A. K. Qin, Gayathri C +3
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approxima…