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Communications of the ACM is a peer-reviewed journal published by ACM, covering Parallel Computing and Optimization Techniques, Algorithms and Data Compression, Distributed and Parallel Computing Systems.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. Communications of the ACM 12, 3, Article 45 (2023).
Formats any DOI in Communications of the ACM style. No sign-up. |
| Publishes research in | Parallel Computing and Optimization Techniques Algorithms and Data Compression Distributed and Parallel Computing Systems Information Systems Education and Curriculum Development Logic, programming, and type systems |
| ISSN | 0001-0782 |
| Citation impact (2-yr) | 3.83 |
| h-index | 434 |
| i10-index | 7,109 |
| Total citations | 1,213,804 |
| Top institutions publishing here | University of California, Berkeley |
| Journal website | cacm.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60…
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion…
MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures,…
Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet…
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution.…
