The key insight of the "Generative Adversarial Networks," by Ian Goodfellow et al., is to learn a generative model's loss function at the same time as learning...Alexei A. Efros, Aaron Hertzmann From Communications of the ACM | November 2020
In this overview paper, we describe one particular approach to unsupervised learning via generative modeling called generative adversarial networks. We briefly...Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio From Communications of the ACM | November 2020
There are few algorithms for multi-flow graphs beyond flow accumulation. The authors of "Flood-Risk Analysis on Terrains" take a big step to fill this knowledge...Shashi Shekhar From Communications of the ACM | September 2020
In this paper, we study a number of flood-risk related problems, give an overview of efficient algorithms for them, as well as explore the efficacy and efficiency...Aaron Lowe, Pankaj K. Agarwal, Mathias Rav From Communications of the ACM | September 2020
"Computing Value of Spatiotemporal Information," by Heba Aly et al., describes a technique for computing the monetary value of a person's location data for a potential...Cyrus Shahabi From Communications of the ACM | September 2020
We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase.
...Heba Aly, John Krumm, Gireeja Ranade, Eric Horvitz From Communications of the ACM | September 2020
Magellan's key insight is that a successful entity matching system must offer a versatile system building paradigm for entity matching that can be easily adapted...Wang-Chiew Tan From Communications of the ACM | August 2020
Entity matching can be viewed as a special class of data science problems and thus can benefit from system building ideas in data science.
AnHai Doan, Pradap Konda, Paul Suganthan G. C., Yash Govind, Derek Paulsen, Kaushik Chandrasekhar, Philip Martinkus, Matthew Christie From Communications of the ACM | August 2020
"Data-Driven Algorithm Design," by Rishi Gupta and Tim Roughgarden, addresses the issue that the best algorithm to use for many problems depends on what the input...Avrim Blum From Communications of the ACM | June 2020
The envy-free cake-cutting problem stood its ground for two decades, until it was cracked by Aziz and Mackenzie. Their solution is presented in "A Bounded and Envy...Ariel D. Procaccia From Communications of the ACM | April 2020
We report on our algorithm that resolved the well-studied cake cutting problem in which the goal is to find an envy-free allocation of a divisible resource based...Haris Aziz, Simon Mackenzie From Communications of the ACM | April 2020
"Automating Visual Privacy Protection Using a Smart LED," presents a new technique to address the issue of cameras capturing proprietary or private information—it...Marco Gruteser From Communications of the ACM | February 2020
We introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms...Shilin Zhu, Chi Zhang, Xinyu Zhang From Communications of the ACM | February 2020
DeepXplore brings a software testing perspective to deep neural networks and, in doing so, creates the opportunity for enormous amounts of follow-on work in several...David G. Andersen From Communications of the ACM | November 2019
We design, implement, and evaluate DeepXplore, the first white-box framework for systematically testing real-world deep learning systems.
Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana From Communications of the ACM | November 2019
The authors of "OpenFab" propose to revisit the processing pipeline that turns a 3D model into machine instructions in light of the solutions developed in computer...Sylvain Lefebvre From Communications of the ACM | September 2019
We present OpenFab, a programmable pipeline for synthesis of multimaterial 3D printed objects that is inspired by RenderMan and modern GPU pipelines.
Kiril Vidimče, Szu-Po Wang, Jonathan Ragan-Kelley, Wojciech Matusik From Communications of the ACM | September 2019
Demand for more powerful big data analytics solutions has spurred the development of novel programming models, abstractions, and platforms. "Scaling Machine Learning...Zachary G. Ives From Communications of the ACM | May 2019
General-purpose compression struggles to achieve both good compression ratios and fast decompression for blockwise uncompressed operations. Therefore, we introduce...Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald From Communications of the ACM | May 2019