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PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
- Yang Song, Taesup Kim, S. Nowozin, S. Ermon, Nate Kushman
- Computer ScienceICLR
- 30 October 2017
Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models. What makes them so special in the eyes of…
Learning to Automatically Solve Algebra Word Problems
- Nate Kushman, Luke Zettlemoyer, R. Barzilay, Yoav Artzi
- Computer ScienceACL
- 1 June 2014
TLDR
Learning to Solve Arithmetic Word Problems with Verb Categorization
- Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, Nate Kushman
- Computer ScienceEMNLP
- 1 October 2014
TLDR
R-BGP: Staying Connected in a Connected World
- Nate Kushman, Srikanth Kandula, D. Katabi, B. Maggs
- Computer ScienceNSDI
- 11 April 2007
TLDR
Constructing Unrestricted Adversarial Examples with Generative Models
- Yang Song, Rui Shu, Nate Kushman, S. Ermon
- Computer ScienceNeurIPS
- 1 May 2018
TLDR
ZipTx: Harnessing Partial Packets in 802.11 Networks
- K. Lin, Nate Kushman, D. Katabi
- Computer ScienceMobiCom '08
- 14 September 2008
TLDR
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
- N. Locascio, Karthik Narasimhan, E. DeLeon, Nate Kushman, R. Barzilay
- Computer ScienceEMNLP
- 9 August 2016
TLDR
MAWPS: A Math Word Problem Repository
- Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, Hannaneh Hajishirzi
- Computer ScienceNAACL
- 12 June 2016
TLDR
Learning Robust Representations via Multi-View Information Bottleneck
- M. Federici, Anjan Dutta, Patrick Forr'e, Nate Kushman, Zeynep Akata
- Computer ScienceICLR
- 17 February 2020
TLDR
Learning to share: narrowband-friendly wideband networks
- H. Rahul, Nate Kushman, D. Katabi, C. Sodini, F. Edalat
- Computer ScienceSIGCOMM '08
- 17 August 2008
TLDR
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