Forrest N. Iandola

Q: What Schools Are Affiliated With Forrest N. Iandola

Forrest N. Iandola is affiliated with the following schools: University of California, Berkeley, University of Illinois Urbana-Champaign

Forrest N. Iandola's AcademicInfluence.com Rankings

Forrest N. Iandola

Engineering

#8064

World Rank

#9537

Historical Rank

#1431

USA Rank

Electrical Engineering

#2600

World Rank

#2729

Historical Rank

#339

USA Rank

engineering Degrees

Forrest N. Iandola

Computer Science

#10521

World Rank

#11054

Historical Rank

#1840

USA Rank

Algorithms

#508

World Rank

#514

Historical Rank

#56

USA Rank

Artificial Intelligence

#6214

World Rank

#6317

Historical Rank

#364

USA Rank

Database

#9620

World Rank

#10158

Historical Rank

#1379

USA Rank

computer-science Degrees

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Engineering
Computer Science

Forrest N. Iandola's Degrees

PhD Electrical Engineering and Computer Sciences University of California, Berkeley
Masters Electrical Engineering and Computer Sciences University of California, Berkeley
Bachelors Electrical Engineering and Computer Sciences University of California, Berkeley

Why Is Forrest N. Iandola Influential?

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According to Wikipedia, Forrest N. Iandola is an American computer scientist specializing in efficient AI. Career Iandola earned a PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016, advised by Kurt Keutzer. As part of his dissertation he co-authored SqueezeNet, a deep neural network for image classification that is optimized for smartphones and other mobile devices.

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Forrest N. Iandola's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size (2016) (5225)
From captions to visual concepts and back (2014) (1207)
- LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE (2016) (672)
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids (2014) (618)
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving (2016) (441)
Deformable part models are convolutional neural networks (2014) (409)
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters (2015) (284)
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction (2013) (239)
How to scale distributed deep learning? (2016) (115)
SqueezeBERT: What can computer vision teach NLP about efficient neural networks? (2020) (79)
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer (2015) (78)
SqueezeNAS: Fast Neural Architecture Search for Faster Semantic Segmentation (2019) (53)
Shallow Networks for High-accuracy Road Object-detection (2016) (46)
Optimal load management system for Aircraft Electric Power distribution (2013) (35)
Keynote: small neural nets are beautiful: enabling embedded systems with small deep-neural- network architectures (2017) (31)
Communication-minimizing 2D convolution in GPU registers (2013) (28)
Audio-Based Multimedia Event Detection with DNNs and Sparse Sampling (2015) (16)
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale (2016) (16)
Boda-RTC: Productive generation of portable, efficient code for convolutional neural networks on mobile computing platforms (2016) (8)
DSCnet: Replicating Lidar Point Clouds With Deep Sensor Cloning (2018) (8)
libHOG: Energy-Efficient Histogram of Oriented Gradient Computation (2015) (6)
DenseNet : Implementing Efficient ConvNet Descriptor Pyramids Technical Report (2014) (4)
Quantifying the Energy Efficiency of Object Recognition and Optical Flow (2014) (4)
Electron Beam Focusing for the International Linear Collider (2008) (3)
PyMercury: Interactive Python for the Mercury Monte Carlo Particle Transport Code (2010) (3)
STOMP: Statistical Techniques for Optimizing and Modeling Performance of Blocked Sparse Matrix Vector Multiplication (2016) (2)
REPRESENTING RANGE COMPENSATORS IN THE TOPAS MONTE CARLO SYSTEM (2012) (1)
Representing Range Compensators with Computational Geometry in TOPAS (2011) (0)
Lab41 Reading Group: SqueezeNet (2016) (0)
Deformable Part Models are Convolutional Neural Networks Tech report (2014) (0)

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Other Resources About Forrest N. Iandola

en.wikipedia.org

What Schools Are Affiliated With Forrest N. Iandola?

Forrest N. Iandola is affiliated with the following schools:

Forrest N. Iandola's Academic­Influence.com Rankings

Forrest N. Iandola's Degrees

Why Is Forrest N. Iandola Influential?

Forrest N. Iandola's Published Works

Published Works

Other Resources About Forrest N. Iandola

What Schools Are Affiliated With Forrest N. Iandola?

Forrest N. Iandola's AcademicInfluence.com Rankings