activity
20202022
most citedFetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals

9 citations · 11 across the 6 of their papers we have counts for

collaborators

7 papers

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…

eess.SP20219 cited

Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals

Reza Khanmohammadi, Mitra Sadat Mirshafiee, Mohammad Mahdi Ghassemi +1

Phonocardiogram (PCG) signal analysis is a critical, widely-studied technology to noninvasively analyze the heart's mechanical activity. Through evaluating heart sounds, this techn…

cs.CL2021

SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations

Hooman Sedghamiz, Shivam Raval, Enrico Santus +2

While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised al…

cs.CL2021

Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media

Shivam Raval, Hooman Sedghamiz, Enrico Santus +3

Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes i…