6.5k citations · 7.6k across the 11 of their papers we have counts for
15 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…
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
Forrest Iandola
In recent years, the research community has discovered that deep neural networks (DNNs) and convolutional neural networks (CNNs) can yield higher accuracy than all previous solutio…
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Bichen Wu, Alvin Wan, Forrest Iandola +2
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time…
How to scale distributed deep learning?
Peter H. Jin, Qiaochu Yuan, Forrest Iandola +1
Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification…
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…
Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms
Matthew Moskewicz, Forrest Iandola, Kurt Keutzer
The popularity of neural networks (NNs) spans academia, industry, and popular culture. In particular, convolutional neural networks (CNNs) have been applied to many image based mac…
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz +3
Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures tha…
FireCaffe: near-linear acceleration of deep neural network training on compute clusters
Forrest N. Iandola, Khalid Ashraf, Matthew W. Moskewicz +1
Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. In this paper we presen…
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
Forrest N. Iandola, Anting Shen, Peter Gao +1
Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab…
From Captions to Visual Concepts and Back
Hao Fang, Saurabh Gupta, Forrest Iandola +9
This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dat…
Deformable Part Models are Convolutional Neural Networks
Ross Girshick, Forrest Iandola, Trevor Darrell +1
Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are…
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Forrest Iandola, Matt Moskewicz, Sergey Karayev +3
Convolutional Neural Networks (CNNs) can provide accurate object classification. They can be extended to perform object detection by iterating over dense or selected proposed objec…