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Technical Perspective: The Quest for Optimal Multi-Item Auctions
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...

Optimal Auctions Through Deep Learning
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...

Technical Perspective: An Elegant Model for Deriving Equations
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...

Deriving Equations from Sensor Data Using Dimensional Function Synthesis
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: Race Logic Presents a Novel Form of Encoding

"In-Sensor Classification With Boosted Race Trees," by Georgios Tzimpragos, et al., proposes a surprising, novel, and creative approach to post-Moore's Law computing...

In-Sensor Classification With Boosted Race Trees
From Communications of the ACM

In-Sensor Classification With Boosted Race Trees

We demonstrate the potential of a novel form of encoding, race logic, in which information is represented as the delay in the arrival of a signal.

From Communications of the ACM

Technical Perspective: A Chiplet Prototype System for Deep Learning Inference

"Simba," by Yakun Sophia Shao, et al., presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to...

Simba
From Communications of the ACM

Simba: Scaling Deep-Learning Inference with Chiplet-Based Architecture

This work investigates and quantifies the costs and benefits of using multi-chip-modules with fine-grained chiplets for deep learning inference, an application...

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: 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...
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