Sign In

Communications of the ACM

Latest Research



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...

Succinct Range Filters
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...

Understanding Deep Learning (Still) Requires Rethinking Generalization
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...

Enabling AI at the Edge with XNOR-Networks
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...

Generative Adversarial Networks
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...

Flood-Risk Analysis on Terrains
From Communications of the ACM

Flood-Risk Analysis on Terrains

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...

From Communications of the ACM

Technical Perspective: Computing the Value of Location Data

"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...

Computing Value of Spatiotemporal Information
From Communications of the ACM

Computing Value of Spatiotemporal Information

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. ...

From Communications of the ACM

Technical Perspective: Entity Matching with Magellan

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...

Magellan
From Communications of the ACM

Magellan: Toward Building Ecosystems of Entity Matching Solutions

Entity matching can be viewed as a special class of data science problems and thus can benefit from system building ideas in data science.

From Communications of the ACM

Technical Perspective: Algorithm Selection as a Learning Problem

"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...

Data-Driven Algorithm Design
From Communications of the ACM

Data-Driven Algorithm Design

We model the problem of identifying a good algorithm from data as a statistical learning problem.

From Communications of the ACM

Technical Perspective: An Answer to Fair Division's Most Enigmatic Question

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...

A Bounded and Envy-Free Cake Cutting Algorithm
From Communications of the ACM

A Bounded and Envy-Free Cake Cutting Algorithm

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...

From Communications of the ACM

Technical Perspective: Lighting the Way to Visual Privacy

"Automating Visual Privacy Protection Using a Smart LED," presents a new technique to address the issue of cameras capturing proprietary or private information—it...

Automating Visual Privacy Protection Using a Smart LED
From Communications of the ACM

Automating Visual Privacy Protection Using a Smart LED

We introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms...
Sign In for Full Access
» Forgot Password? » Create an ACM Web Account