"On Sampled Metrics for Item Recommendation," by Walid Krichene and Steffen Rendle, exposes a crucial aspect for the evaluation of algorithms and tools: the impact...Fabio Vandin From Communications of the ACM | July 2022
This paper investigates sampled metrics and shows that it is possible to improve the quality of sampled metrics by applying a correction, obtained by minimizing...Walid Krichene, Steffen Rendle From Communications of the ACM | July 2022
In "FANG," the authors focus on a strategy of automatically detecting disinformation campaigns on online media with a new graph-based, contextual technique for...Philippe Cudré-Mauroux From Communications of the ACM | April 2022
We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection.
Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, Min-Yen Kan From Communications of the ACM | April 2022
"Worst-Case Topological Entropy and Minimal Data Rate for State Estimation of Switched Linear Systems" gives a method for computing the topological entropy of a...Sayan Mitra From Communications of the ACM | February 2022
In this paper, we study the problem of estimating the state of a switched linear system when the observation of the system is subject to communication constraints...Guillaume O. Berger, Raphael M. Jungers From Communications of the ACM | February 2022
Neural volume rendering exploded onto the scene in 2020, triggered by "NeRF," the impressive paper by Ben Mildenhall et al., on Neural Radiance Fields.
Frank Dellaert From Communications of the ACM | January 2022
We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene...Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng From Communications of the ACM | January 2022
"WINOGRANDE" explores new methods of dataset development and adversarial filtering, expressly designed to prevent AI systems from making claims of smashing through...Leora Morgenstern From Communications of the ACM | September 2021
We introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original Winograd Schema Challenge, but adjusted to improve both the scale and the...Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi From Communications of the ACM | September 2021
"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...Constantinos Daskalakis From Communications of the ACM | August 2021
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...Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai S. Ravindranath From Communications of the ACM | August 2021
"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships...Sriram Sankaranarayanan From Communications of the ACM | July 2021
We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical...Vasileios Tsoutsouras, Sam Willis, Phillip Stanley-Marbell From Communications of the ACM | July 2021
"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...Abhishek Bhattacharjee From Communications of the ACM | June 2021
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.
Georgios Tzimpragos, Advait Madhavan, Dilip Vasudevan, Dmitri Strukov, Timothy Sherwood From Communications of the ACM | June 2021
"Simba," by Yakun Sophia Shao, et al., presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to...Natalie Enright Jerger From Communications of the ACM | June 2021
This work investigates and quantifies the costs and benefits of using multi-chip-modules with fine-grained chiplets for deep learning inference, an application...Yakun Sophia Shao, Jason Cemons, Rangharajan Venkatesan, Brian Zimmer, Matthew Fojtik, Nan Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen G. Tell, Yanqing Zhang, William J. Dally, Joel Emer, C. Thomas Gray, Brucek Khailany, Stephen W. Keckler From Communications of the ACM | June 2021
"Understanding Deep Learning (Still) Requires Rethinking Generalization," Chiyuan Zhang, et al., brings a fundamental new theoretical challenge: Why don't today's...Sanjeev Arora From Communications of the ACM | March 2021
In this work, we presented a simple experimental framework for interrogating purported measures of generalization.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals From Communications of the ACM | March 2021