19 citations · 23 across the 2 of their papers we have counts for
5 papers
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Forrest N. Iandola, Albert E. Shaw, Ravi Krishna +1
Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, nat…
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
Albert Shaw, Daniel Hunter, Forrest Iandola +1
For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target co…
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning
Paden Tomasello, Sammy Sidhu, Anting Shen +6
Convolutional neural networks (CNNs) have become increasingly popular for solving a variety of computer vision tasks, ranging from image classification to image segmentation. Recen…
Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures
Forrest Iandola, Kurt Keutzer
Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a thr…
Shallow Networks for High-Accuracy Road Object-Detection
Khalid Ashraf, Bichen Wu, Forrest N. Iandola +2
The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques fo…