4 papers
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…