Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Allan Heydon

dblp:48/3474 · DBLP profile ↗
← Back
8ranked-venue papers
4as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 since 2021Computer networks · 2Software engineering, systems software and programming languages · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 2 · 1 since 2021Systems, architecture and hardware · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Deep learning architectures and training · 47% Graph learning · 47% Representation and self-supervised learning · 6%
Software engineering, system software, and programming languages
2 papers
Programming languages and type systems · 64% Compilers and program optimization · 19% Requirements engineering and software design · 16%

Topics — the 4 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning
graph regularization
0.512021
Neural Structured Learning: Training Neural Networks with Structured Signals · WSDM 2021
Machine learning › Representation and self-supervised learning › representation learning
embedding learning
0.112020
Neural Structured Learning: Training Neural Networks with Structured Signals · KDD 2020
Systems and software security
file system security
0.011990
Miró: Visual Specification of Security · IEEE Trans. Software Eng. 1990
Systems and software security
security policy specification
0.011990
Miró: Visual Specification of Security · IEEE Trans. Software Eng. 1990

Methods — techniques the papers use, named apart from their topics

adversarial perturbation · 0.9embedding learning · 0.5graph regularization · 0.4precise dependency tracking · 0.0memoization · 0.0visual language · 0.0constraint language · 0.0
YearPublicationVenuePosition
2021 Neural Structured Learning: Training Neural Networks with Structured Signals
abstract
We present Neural Structured Learning (NSL) in TensorFlow [1], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [2] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
Arjun Gopalan, Da-Cheng Juan, Cesar Ilharco Magalhaes, Chun-Sung Ferng, Allan Heydon, Chun-Ta Lu, Philip Pham, George Yu, Yicheng Fan
WSDM5
2020 Neural Structured Learning: Training Neural Networks with Structured Signals
abstract
We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [3] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
Arjun Gopalan, Da-Cheng Juan, Cesar Ilharco Magalhaes, Chun-Sung Ferng, Allan Heydon, Chun-Ta Lu, Philip Pham, George Yu
KDD5
2000 Caching function calls using precise dependencies
abstract
This paper describes the implementation of a purely functional programming language for building software systems. In this language, external tools like compilers and linkers are invoked by function calls. Because some function calls are extremely expensive, it is obviously important to reuse the results of previous function calls whenever possible. Caching a function call requires the language interpreter to record all values on which the function call depends. For optimal caching, it is important to record precise dependencies that are both dynamic and fine-grained. The paper sketches how we compute such dependencies, describes the implementation of an e#cient function cache, and evaluates our implementation's performance. 1 Introduction We consider the problem of implementing a pure functional language in which some function calls are expected to be extremely costly. This problem arises in the context of the Vesta software configuration management system, a system for managing and...
Allan Heydon, Roy Levin
PLDI1
2000 On near-uniform URL sampling
Monika Henzinger, Allan Heydon, Michael Mitzenmacher, Marc Najork
Comput. Networks2
2000 Performance limitations of the Java core libraries
abstract
Unlike applets, traditional systems programs written in Java place significant demands on the Java runtime and core libraries, and their performance is often critically important. This paper describes our experiences using Java to build such a systems program, namely, a high-performance web crawler. We found that our runtime, which includes a just-in-time compiler that compiles Java bytecodes to native machine code, performed well. However, we encountered several performance problems with the Java core libraries, including excessive synchronization, excessive allocation, and other inefficiencies. The paper describes the most serious pitfalls and how we programmed around them. In total, these workarounds more than tripled the speed of our crawler. Copyright © 2000 John Wiley & Sons, Ltd.
Allan Heydon, Marc Najork
Concurr. Pract. Exp.1
1999 Measuring Index Quality Using Random Walks on the Web
Monika Henzinger, Allan Heydon, Michael Mitzenmacher, Marc Najork
Comput. Networks2
1999 Mercator: A Scalable, Extensible Web Crawler
Allan Heydon, Marc Najork
World Wide Web1
1990 Miró: Visual Specification of Security
abstract
Miro is a set of languages and tools that support the visual specification of file system security. Two visual languages are presented: the instance language, which allows specification of file system access, and the constraint language, which allows specification of security policies. Miro visual languages and tools are used to specify security configurations. A visual language is one whose entities are graphical, such as boxes and arrows, specifying means stating independently of any implementation the desired properties of a system. Security means file system protection: ensuring that files are protected from unauthorized access and granting privileges to some users, but not others. Tools implemented and examples of how these languages can be applied to real security specification problems are described.>
Allan Heydon, Mark W. Maimone, J. D. Tygar, Jeannette M. Wing, Amy Moormann Zaremski
IEEE Trans. Software Eng.1