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From Communications of the ACM

Technical Perspective: Breaking the Mold of Machine Learning

"Never-Ending Learning" is the latest and one of the most compelling incarnations of Tom Mitchell and his collaborators' research investigating how to broaden the...

Never-Ending Learning
From Communications of the ACM

Never-Ending Learning

In this paper we define more precisely the never-ending learning paradigm for machine learning, and present one case study: the Never-Ending Language Learner (NELL)...

From Communications of the ACM

Technical Perspective: Expressive Probabilistic Models and Scalable Method of Moments

The authors of "Learning Topic Models—Provably and Efficiently," developed a new method for fitting topic models and at large scale.

Learning Topic Models – Provably and Efficiently
From Communications of the ACM

Learning Topic Models – Provably and Efficiently

This article shows that some new theoretical algorithms that have provable guarantees can be adapted to yield highly practical tools for topic modeling.

From Communications of the ACM

Technical Perspective: Can High Performance Be Portable?

"Halide: Decoupling Algorithms from Schedules for High-Performance Image Processing" by Ragan-Kelley et al. on the image processing language Halide explores a substantially...

Halide
From Communications of the ACM

Halide: Decoupling Algorithms from Schedules For High-Performance Image Processing

We propose a new programming language for image processing pipelines, called Halide, that separates the algorithm from its schedule.

From Communications of the ACM

Technical Perspective: Exploring a Kingdom By Geodesic Measures

"The Heat Method for Distance Computation," by Crane, Weischedel, and Wardetzky, shows that the gradient of the probability density function of a random walk is...

The Heat Method For Distance Computation
From Communications of the ACM

The Heat Method For Distance Computation

We introduce the heat method for solving the single- or multiple-source shortest path problem on both flat and curved domains.

From Communications of the ACM

Technical Perspective: Linking Form, Function, and Fabrication

To avoid costly feedback loops between design, engineering, and fabrication, research in computer graphics has recently tried to incorporate key aspects of function...

Spin-It
From Communications of the ACM

Spin-It: Optimizing Moment of Inertia For Spinnable Objects

In this article, we describe an algorithm to generate designs for spinning objects by optimizing their mass distribution.

From Communications of the ACM

Technical Perspective: Unexpected Connections

The inherent scalability of an interface is the focus of "The Scalable Commutativity Rule" by Austin T. Clements, et al.

The Scalable Commutativity Rule
From Communications of the ACM

The Scalable Commutativity Rule: Designing Scalable Software For Multicore Processors

This paper introduces an interface-driven approach to building scalable software.

From Communications of the ACM

Technical Perspective: Ironfleet Simplifies Proving Safety and Liveness Properties

"IronFleet: Proving Safety and Liveness of Practical Distributed Systems," by Chris Hawblitzel, et al., describes mechanically checked proofs for two non-trivial...

Ironfleet
From Communications of the ACM

Ironfleet: Proving Safety and Liveness of Practical Distributed Systems

We demonstrate the methodology on a complex implementation of a Paxos-based replicated state machine library and a lease-based sharded key-value store. With our...

From Communications of the ACM

Technical Perspective: Low-Depth Arithmetic Circuits

The past few years have seen a revolution in our understanding of arithmetic circuits. "Unexpected Power of Low-Depth Arithmetic Circuits" by Gupta et al. on the...

Unexpected Power of Low-Depth Arithmetic Circuits
From Communications of the ACM

Unexpected Power of Low-Depth Arithmetic Circuits

Several earlier results have shown that it is possible to rearrange basic computational elements in surprising ways to give more efficient algorithms. The main...

From Communications of the ACM

Technical Perspective: What Led Computer Vision to Deep Learning?

We are in the middle of the third wave of interest in artificial neural networks as the leading paradigm for machine learning. "ImageNet Classification with Deep...

Imagenet Classification with Deep Convolutional Neural Networks
From Communications of the ACM

Imagenet Classification with Deep Convolutional Neural Networks

In the 1980s backpropagation did not live up to the very high expectations of its advocates. Twenty years later, we know what went wrong: for deep neural networks...

Deepdive
From Communications of the ACM

Deepdive: Declarative Knowledge Base Construction

We describe DeepDive, a system that combines database and machine learning ideas to help to develop knowledge base construction systems.

From Communications of the ACM

Technical Perspective: Functional Compilers

"Exploiting Vector Instructions with Generalized Stream Fusion" points out that stream fusion by itself is not well suited for generating bulk instructions such...
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