In "Coz: Finding Code that Counts with Causal Profiling," Curtsinger and Berger describe causal profiling, which tell programmers exactly how much speed-up bang...Landon P. Cox From Communications of the ACM | June 2018
This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts...Charlie Curtsinger, Emery D. Berger From Communications of the ACM | June 2018
"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...Oren Etzioni From Communications of the ACM | May 2018
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)...T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, B. Yang, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling From Communications of the ACM | May 2018
This article shows that some new theoretical algorithms that have provable guarantees can be adapted to yield highly practical tools for topic modeling.
Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu From Communications of the ACM | April 2018
"Halide: Decoupling Algorithms from Schedules for High-Performance Image Processing" by Ragan-Kelley et al. on the image processing language Halide explores a substantially...Manuel Chakravarty From Communications of the ACM | January 2018
We propose a new programming language for image processing pipelines, called Halide, that separates the algorithm from its schedule.
Jonathan Ragan-Kelley, Andrew Adams, Dillon Sharlet, Connelly Barnes, Sylvain Paris, Marc Levoy, Saman Amarasinghe, Frédo Durand From Communications of the ACM | January 2018
"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...Marc Alexa From Communications of the ACM | November 2017
We introduce the heat method for solving the single- or multiple-source shortest path problem on both flat and curved domains.
Keenan Crane, Clarisse Weischedel, Max Wardetzky From Communications of the ACM | November 2017
To avoid costly feedback loops between design, engineering, and fabrication, research in computer graphics has recently tried to incorporate key aspects of function...Helmut Pottmann From Communications of the ACM | August 2017
In this article, we describe an algorithm to generate designs for spinning objects by optimizing their mass distribution.
Moritz Bächer, Bernd Bickel, Emily Whiting, Olga Sorkine-Hornung From Communications of the ACM | August 2017
This paper introduces an interface-driven approach to building scalable software.
Austin T. Clements, M. Frans Kaashoek, Eddie Kohler, Robert T. Morris, Nickolai Zeldovich From Communications of the ACM | August 2017
"IronFleet: Proving Safety and Liveness of Practical Distributed Systems," by Chris Hawblitzel, et al., describes mechanically checked proofs for two non-trivial...Fred B. Schneider From Communications of the ACM | July 2017
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...Chris Hawblitzel, Jon Howell, Manos Kapritsos, Jacob R. Lorch, Bryan Parno, Michael L. Roberts, Srinath Setty, Brian Zill From Communications of the ACM | July 2017
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...Avi Wigderson From Communications of the ACM | June 2017
Several earlier results have shown that it is possible to rearrange basic computational elements in surprising ways to give more efficient algorithms. The main...Ankit Gupta, Pritish Kamath, Neeraj Kayal, Ramprasad Saptharishi From Communications of the ACM | June 2017
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...Jitendra Malik From Communications of the ACM | June 2017
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...Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton From Communications of the ACM | June 2017