5 papers
A Generalisation Signal Need Not Be a Model-Selection Signal
Aditya Nagarsekar, M P Ashish Bhat, Aadi Nesarkar +5
Model selection in computational biology often relies on validation data drawn from the training regime, even when deployment lies outside it. When validation no longer preserves w…
Breaking Chains with Trees: Model-Parallel Deep Learning with Time Complexity
Neeraj Mohan Sushma, Aditya Nagarsekar, Cabrel Teguemne Fokam +4
Modern deep neural networks are trained using error backpropagation, which requires sequential forward and backward computations across network layers. As these networks become dee…
: A library for Linear RNNs
Karan Bania, Soham Kalburgi, Manit Tanwar +8
Linear recurrent neural networks (LRNNs) provide a structured approach to sequence modeling that bridges classical linear dynamical systems and modern deep learning, offering both…
Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models
Anish Sathyanarayanan, Aditya Nagarsekar, Aarush Rathore
Chain-of-thought (CoT) prompting is widely used as a reasoning aid and is often treated as a transparency mechanism. Yet behavioral gains under CoT do not imply that the model's in…
eDCF: Estimating Intrinsic Dimension using Local Connectivity
Dhruv Gupta, Aditya Nagarsekar, Vraj Shah +1
Modern datasets often contain high-dimensional features exhibiting complex dependencies. To effectively analyze such data, dimensionality reduction methods rely on estimating the d…