The automated blood pressure wearable system described in "eBP," by Nam Bui et al., is a sterling example of the third wave of mobile health tech to fill the preventative...Josiah D. Hester From Communications of the ACM | August 2021
We developed eBP to measure blood pressure from inside a user's ear aiming to minimize the measurement's impact on normal activities while maximizing its comfort...Nam Bui, Nhat Pham, Jessica Jacqueline Barnitz, Zhanan Zou, Phuc Nguyen, Hoang Truong, Taeho Kim, Nicholas Farrow, Anh Nguyen, Jianliang Xiao, Robin Deterding, Thang Dinh, Tam Vu From Communications of the ACM | August 2021
"Optimal Auctions Through Deep Learning," by Paul Dütting et al., contributes a very interesting and forward-looking new take on the optimal multi-item mechanism...Constantinos Daskalakis From Communications of the ACM | August 2021
We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions...Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai S. Ravindranath From Communications of the ACM | August 2021
"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships...Sriram Sankaranarayanan From Communications of the ACM | July 2021
We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical...Vasileios Tsoutsouras, Sam Willis, Phillip Stanley-Marbell From Communications of the ACM | July 2021
The authors of "Succinct Range Filters" make a critical and insightful observation: For a given set of queries, the upper levels of the trie incur many more accesses...Stratos Idreos From Communications of the ACM | April 2021
We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests.
Huanchen Zhang, Hyeontaek Lim, Viktor Leis, David G. Andersen, Michael Kaminsky, Kimberly Keeton, Andrew Pavlo From Communications of the ACM | April 2021
"Understanding Deep Learning (Still) Requires Rethinking Generalization," Chiyuan Zhang, et al., brings a fundamental new theoretical challenge: Why don't today's...Sanjeev Arora From Communications of the ACM | March 2021
In this work, we presented a simple experimental framework for interrogating purported measures of generalization.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals From Communications of the ACM | March 2021
How to produce a convolutional neural net that is small enough to run on a mobile device, and accurate enough to be worth using? The strategies in "Enabling AI...David Alexander Forsyth From Communications of the ACM | December 2020
We present a novel approach to running state-of-the-art AI algorithms in edge devices, and propose two efficient approximations to standard convolutional neural...Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi From Communications of the ACM | December 2020
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