3 citations · 5 across the 3 of their papers we have counts for
8 papers
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…
WW-Nets: Dual Neural Networks for Object Detection
Mohammad K. Ebrahimpour, J. Ben Falandays, Samuel Spevack +2
We propose a new deep convolutional neural network framework that uses object location knowledge implicit in network connection weights to guide selective attention in object detec…
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning
Jacob Rafati, David C. Noelle
Efficient exploration for automatic subgoal discovery is a challenging problem in Hierarchical Reinforcement Learning (HRL). In this paper, we show that intrinsic motivation learni…
Learning sparse representations in reinforcement learning
Jacob Rafati, David C. Noelle
Reinforcement learning (RL) algorithms allow artificial agents to improve their selection of actions to increase rewarding experiences in their environments. Temporal Difference (T…