Eulerian video magnification for revealing subtle changes in the world
- Hao-Yu Wu, Michael Rubinstein, Eugene Shih, J. Guttag, F. Durand, W. Freeman
- Computer ScienceACM Transactions on Graphics
- 1 July 2012
Using the Eulerian Video Magnification method, the flow of blood as it fills the face is able to be visualize and the resulting signal is amplified to reveal hidden information.
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
- Guha Balakrishnan, Amy Zhao, M. Sabuncu, J. Guttag, Adrian V. Dalca
- Computer ScienceIEEE Transactions on Medical Imaging
- 14 September 2018
VoxelMorph promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications and demonstrates that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster.
An Unsupervised Learning Model for Deformable Medical Image Registration
- Guha Balakrishnan, Amy Zhao, M. Sabuncu, J. Guttag, Adrian V. Dalca
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 7 February 2018
The proposed method uses a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field, and demonstrates registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice.
Cutting the electric bill for internet-scale systems
- Asfandyar Qureshi, Rick Weber, H. Balakrishnan, J. Guttag, B. Maggs
- EconomicsConference on Applications, Technologies…
- 16 August 2009
The variation due to fluctuating electricity prices is characterized and it is argued that existing distributed systems should be able to exploit this variation for significant economic gains.
Application of Machine Learning To Epileptic Seizure Detection
A machine learning approach to constructing patient-specific classifiers that detect the onset of an epileptic seizure through analysis of the scalp EEG, a non-invasive measure of the brain's electrical activity.
Detecting Pulse from Head Motions in Video
- Guha Balakrishnan, F. Durand, J. Guttag
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 23 June 2013
This method tracks features on the head and performs principal component analysis (PCA) to decompose their trajectories into a set of component motions and chooses the component that best corresponds to heartbeats based on its temporal frequency spectrum.
ANTS: a toolkit for building and dynamically deploying network protocols
- D. Wetherall, J. Guttag, D. Tennenhouse
- Computer ScienceIEEE Open Architectures and Network Programming
- 3 April 1998
A novel approach to building and deploying network protocols based on mobile code, demand loading, and caching techniques that allows new protocols to be dynamically deployed at both routers and end systems, without the need for coordination and without unwanted interaction between co-existing protocols.
Larch: Languages and Tools for Formal Specification
- J. Guttag, J. Horning, S. Garland, K. Jones, A. Modet, Jeannette M. Wing
- Computer ScienceTexts and Monographs in Computer Science
- 1993
This monograph discusses the use of formal specifications in program development and introduces the notation of mathematical logic in formal specification languages and supporting tools.
What is the State of Neural Network Pruning?
- Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, J. Guttag
- Computer ScienceConference on Machine Learning and Systems
- 6 March 2020
Issues with current practices in pruning are identified, concrete remedies are suggested, and ShrinkBench, an open-source framework to facilitate standardized evaluations of pruning methods are introduced, to be used to compare various pruning techniques.
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
- Adrian V. Dalca, Guha Balakrishnan, J. Guttag, M. Sabuncu
- Computer ScienceInternational Conference on Medical Image…
- 11 May 2018
This paper presents a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs) and results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates.
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