3 citations · 7 across the 6 of their papers we have counts for
8 papers
Deep Metric Learning with Soft Orthogonal Proxies
Farshad Saberi-Movahed, Mohammad K. Ebrahimpour, Farid Saberi-Movahed +5
Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compare…
Image-based eeg classification of brain responses to song recordings
Adolfo G. Ramirez-Aristizabal, Mohammad K. Ebrahimpour, Christopher T. Kello
Classifying EEG responses to naturalistic acoustic stimuli is of theoretical and practical importance, but standard approaches are limited by processing individual channels separat…
Multi-Head Deep Metric Learning Using Global and Local Representations
Mohammad K. Ebrahimpour, Gang Qian, Allison Beach
Deep Metric Learning (DML) models often require strong local and global representations, however, effective integration of local and global features in DML model training is a chal…
InfantNet: A Deep Neural Network for Analyzing Infant Vocalizations
Mohammad K. Ebrahimpour, Sara Schneider, David C. Noelle +1
Acoustic analyses of infant vocalizations are valuable for research on speech development as well as applications in sound classification. Previous studies have focused on measures…
End-to-End Auditory Object Recognition via Inception Nucleus
Mohammad K. Ebrahimpour, Timothy Shea, Andreea Danielescu +2
Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, end-to…
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention
Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu +4
Deep Convolutional Neural Networks (CNNs) have been repeatedly proven to perform well on image classification tasks. Object detection methods, however, are still in need of signifi…