activity
20202026
most citedFetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals

9 citations · 16 across the 18 of their papers we have counts for

collaborators
Showing cs.LGShow all

6 papers · 1 filter

cs.LG2024

Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework

Niloufar Eghbali, Tuka Alhanai, Mohammad M. Ghassemi

Mechanical Ventilation (MV) is a critical life-support intervention in intensive care units (ICUs). However, optimal ventilator settings are challenging to determine because of the…

cs.LG2024

Temporal Link Prediction Using Graph Embedding Dynamics

Sanaz Hasanzadeh Fard, Mohammad Ghassemi

Graphs are a powerful representation tool in machine learning applications, with link prediction being a key task in graph learning. Temporal link prediction in dynamic networks is…

cs.LG20232 cited

The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains

Reza Khanmohammadi, Tuka Alhanai, Mohammad M. Ghassemi

Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to…

cs.LG20222 cited

MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs

Reza Khanmohammadi, Sari Saba-Sadiya, Sina Esfandiarpour +2

In this paper, we present Mambanet: a hybrid neural network for predicting the outcomes of Basketball games. Contrary to other studies, which focus primarily on season games, this…

cs.LG2022

Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach

Allen R. Williams, Yoolim Jin, Anthony Duer +2

In recent years it has become possible to collect GPS data from drivers and to incorporate this data into automobile insurance pricing for the driver. This data is continuously col…

cs.LG2021

Feature Imitating Networks

Sari Saba-Sadiya, Tuka Alhanai, Mohammad M Ghassemi

In this paper, we introduce a novel approach to neural learning: the Feature-Imitating-Network (FIN). A FIN is a neural network with weights that are initialized to reliably approx…