Communications of the ACM
Latest Research
From Communications of the ACM
Technical Perspective: Liquid Testing Using Built-in Phone Sensors
"Liquid Testing with Your Smartphone," by Shichao Yue and Dina Katabi, proposes a novel technique for determining the surface tension of a liquid by leveraging...
From Communications of the ACM
Liquid Testing with Your Smartphone
We show a simple and accurate approach to measuring surface tension that's available to anyone with a smartphone.
From Communications of the ACM
Technical Perspective: The Importance of WINOGRANDE
"WINOGRANDE" explores new methods of dataset development and adversarial filtering, expressly designed to prevent AI systems from making claims of smashing through...
From Communications of the ACM
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
We introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original Winograd Schema Challenge, but adjusted to improve both the scale and the...
From Communications of the ACM
Technical Perspective: eBP Rides the Third Wave of Mobile Health
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...
From Communications of the ACM
eBP: An Ear-Worn Device for Frequent and Comfortable Blood Pressure Monitoring
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...
From Communications of the ACM
Technical Perspective: The Quest for Optimal Multi-Item Auctions
"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...
From Communications of the ACM
Optimal Auctions Through Deep Learning
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...
From Communications of the ACM
Technical Perspective: An Elegant Model for Deriving Equations
"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships...
From Communications of the ACM
Deriving Equations from Sensor Data Using Dimensional Function Synthesis
We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical...
From Communications of the ACM
Technical Perspective: The Strength of SuRF
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...
From Communications of the ACM
Succinct Range Filters
We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests.
From Communications of the ACM
Technical Perspective: Why Don't Today's Deep Nets Overfit to Their Training Data?
"Understanding Deep Learning (Still) Requires Rethinking Generalization," Chiyuan Zhang, et al., brings a fundamental new theoretical challenge: Why don't today's...
From Communications of the ACM
Understanding Deep Learning (Still) Requires Rethinking Generalization
In this work, we presented a simple experimental framework for interrogating purported measures of generalization.
From Communications of the ACM
Technical Perspective: XNOR-Networks – Powerful but Tricky
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...
From Communications of the ACM
Enabling AI at the Edge with XNOR-Networks
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...
From Communications of the ACM
Technical Perspective: When the Adversary Is Your Friend
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...
From Communications of the ACM
Generative Adversarial Networks
In this overview paper, we describe one particular approach to unsupervised learning via generative modeling called generative adversarial networks. We briefly...
From Communications of the ACM
Technical Perspective: Progress in Spatial Computing for Flood Prediction
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...
From Communications of the ACM