Search Results for author:
Found 6 papers, 1 papers with code
Minimizing Factual Inconsistency and Hallucination in Large Language Models
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.
Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents
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.
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity.
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear, smooth latent dynamics from sequential data.
A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS
In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear.
Real-Time Decoding of an Integrate and Fire Encoder
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.
