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cs.LG2026

ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces

Kunal Kumar Pant, Nithin Nagaraj

Transformer models are most often understood through what they do: their benchmark performance, generation quality, or behavior on downstream tasks. Yet frozen transformer input-em…

cs.LG2026

Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities

Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar +1

Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency ra…

cs.LG2026

Linked Data Classification using Neurochaos Learning

Pooja Honna, Ayush Patravali, Nithin Nagaraj +1

Neurochaos Learning (NL) has shown promise in recent times over traditional deep learning due to its two key features: ability to learn from small sized training samples, and low c…

cs.LG2025

Hyperparameter-Free Neurochaos Learning Algorithm for Classification

Akhila Henry, Nithin Nagaraj

Neurochaos Learning (NL) is a brain-inspired classification framework that employs chaotic dynamics to extract features from input data and yields state of the art performance on c…

cs.LG2025

Augmented Regression Models using Neurochaos Learning

Akhila Henry, Nithin Nagaraj

This study presents novel Augmented Regression Models using Neurochaos Learning (NL), where Tracemean features derived from the Neurochaos Learning framework are integrated with tr…

cs.LG2025

Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda

Nanjangud C. Narendra, Nithin Nagaraj

Deep learning implemented via neural networks, has revolutionized machine learning by providing methods for complex tasks such as object detection/classification and prediction. Ho…