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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: 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: A Perspective on Pivot Tracing

Instead of handing trace records off to a collector for long-term storage and future processing, the system described in "Pivot Tracing: Dynamic Causal Monitoring...

Pivot Tracing
From Communications of the ACM

Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems

This paper presents Pivot Tracing, a monitoring framework for distributed systems, which addresses the limitations of today's monitoring and diagnosis tools by...

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...

From Communications of the ACM

Technical Perspective: Bootstrapping a Future of Open Source, Specialized Hardware

OpenPiton research is one of the watershed moments in the fundamental shift toward the construction of an open source ecosystem for implementing prototype chips...

OpenPiton
From Communications of the ACM

OpenPiton: An Open Source Hardware Platform For Your Research

We present OpenPiton, an open source framework for building scalable architecture research prototypes from one core to 500 million cores.

From Communications of the ACM

Technical Perspective: A Whitebox Solution for Blackbox-Like Behaviors

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...

DeepXplore
From Communications of the ACM

DeepXplore: Automated Whitebox Testing of Deep Learning Systems

We design, implement, and evaluate DeepXplore, the first white-box framework for systematically testing real-world deep learning systems.

From Communications of the ACM

Technical Perspective: From Virtual Worlds to Digital Fabrication

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...

OpenFab
From Communications of the ACM

OpenFab: A Programmable Pipeline for Multimaterial Fabrication

We present OpenFab, a programmable pipeline for synthesis of multimaterial 3D printed objects that is inspired by RenderMan and modern GPU pipelines.

From Communications of the ACM

Technical Perspective: Compressing Matrices for Large-Scale Machine Learning

Demand for more powerful big data analytics solutions has spurred the development of novel programming models, abstractions, and platforms. "Scaling Machine Learning...

Compressed Linear Algebra for Declarative Large-Scale Machine Learning
From Communications of the ACM

Compressed Linear Algebra for Declarative Large-Scale Machine Learning

General-purpose compression struggles to achieve both good compression ratios and fast decompression for blockwise uncompressed operations. Therefore, we introduce...
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