2 citations · 3 across the 5 of their papers we have counts for
5 papers
Training with Mixed-Precision Floating-Point Assignments
Wonyeol Lee, Rahul Sharma, Alex Aiken
When training deep neural networks, keeping all tensors in high precision (e.g., 32-bit or even 16-bit floats) is often wasteful. However, keeping all tensors in low precision (e.g…
MinUn: Accurate ML Inference on Microcontrollers
Shikhar Jaiswal, Rahul Kiran Kranti Goli, Aayan Kumar +2
Running machine learning inference on tiny devices, known as TinyML, is an emerging research area. This task requires generating inference code that uses memory frugally, a task th…
Variational Rejection Particle Filtering
Rahul Sharma, Soumya Banerjee, Dootika Vats +1
We present a variational inference (VI) framework that unifies and leverages sequential Monte-Carlo (particle filtering) with \emph{approximate} rejection sampling to construct a f…
Refined -Divergence Variational Inference via Rejection Sampling
Rahul Sharma, Abhishek Kumar, Piyush Rai
We present an approximate inference method, based on a synergistic combination of Rényi -divergence variational inference (RDVI) and rejection sampling (RS). RDVI is based on mi…
Regularization and Variable Selection with Copula Prior
Rahul Sharma, Sourish Das
In this work, we show that under specific choices of the copula, the lasso, elastic net, and -prior are particular cases of `copula prior,' for regularization and variable selec…