278 citations · 311 across the 7 of their papers we have counts for
7 papers
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions
Ehsan Gholami, Mohammad Motamedi, Ashwin Aravindakshan
The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play crit…
A Data-Centric Approach for Training Deep Neural Networks with Less Data
Mohammad Motamedi, Nikolay Sakharnykh, Tim Kaldewey
While the availability of large datasets is perceived to be a key requirement for training deep neural networks, it is possible to train such models with relatively little data. Ho…
Resource-Scalable CNN Synthesis for IoT Applications
Mohammad Motamedi, Felix Portillo, Mahya Saffarpour +2
State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks a…
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms
Mohammad Motamedi, Felix Portillo, Daniel Fong +1
Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) ha…
Cappuccino: Efficient Inference Software Synthesis for Mobile System-on-Chips
Mohammad Motamedi, Daniel Fong, Soheil Ghiasi
Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped Internet of Things (IoT) devices permeate into every aspec…
Fast and Energy-Efficient CNN Inference on IoT Devices
Mohammad Motamedi, Daniel Fong, Soheil Ghiasi
Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped internet of things (IoT) devices permeate into every aspec…
Hardware-oriented Approximation of Convolutional Neural Networks
Philipp Gysel, Mohammad Motamedi, Soheil Ghiasi
High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promis…