3 papers
cs.LG2026
Rethinking Reservoir Pruning: A Dynamical Perspective for Echo State Networks
Sudip Laudari, Puspa Raj Adhikari
Echo State Networks (ESNs) offer an efficient framework for temporal prediction, but their randomly initialized reservoirs are often over-parameterized and dynamically redundant. E…
cs.CV2026
Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains
Sudip Laudari, Sang Hun Baek
Geometric data augmentation is widely used in segmentation workflows, but polygon annotations are often assumed to remain valid after transformation. This assumption can fail in st…
cs.LG2026
Centrality-Based Pruning for Efficient Echo State Networks
Sudip Laudari
Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, randomly initialized reservoi…