1 citations · 2 across the 3 of their papers we have counts for
3 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…
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
Yudou Tian, Neeraj Mohan Sushma, Harshvardhan Mestha +3
In-context learning lets a sequence model adapt to a new task from examples in its input. A prominent line of work shows how self-attention can be constructed to implement gradient…
Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models
Mark Schöne, Neeraj Mohan Sushma, Jingyue Zhuge +3
Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other v…