no code implementations • 23 Nov 2023 • Muneeswaran I, Shreya Saxena, Siva Prasad, M V Sai Prakash, Advaith Shankar, Varun V, Vishal Vaddina, Saisubramaniam Gopalakrishnan
Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks.
no code implementations • 14 Feb 2023 • Shreya Saxena, Raj Sangani, Siva Prasad, Shubham Kumar, Mihir Athale, Rohan Awhad, Vishal Vaddina
Recent advances in the healthcare industry have led to an abundance of unstructured data, making it challenging to perform tasks such as efficient and accurate information retrieval at scale.
1 code implementation • NeurIPS 2019 • Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski
Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity.
no code implementations • 6 Nov 2018 • Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski
We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear, smooth latent dynamics from sequential data.
no code implementations • 27 Sep 2018 • Daniel Hernandez Diaz, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski
In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear.
no code implementations • NeurIPS 2014 • Shreya Saxena, Munther Dahleh
Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal.