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Mark McLean

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4 papers

2 author rows

ECAI Conference 2023 Conference Paper

An Easy Rejection Sampling Baseline via Gradient Refined Proposals

Edward Raff
Mark McLean
James Holt

Rejection sampling is a common tool for low dimensional problems (d ≤ 2), often touted as an “easy” way to obtain valid samples from a distribution f(·) of interest. In practice it is non-trivial to apply, often requiring considerable mathematical effort to devise a good proposal distribution g(·) and select a supremum C. More advanced samplers require additional mathematical derivations, limitations on f(·), or even cross-validation, making them difficult to apply. We devise a new approximate baseline approach to rejection sampling that works with less information, requiring only a differentiable f(·) be specified, making it easier to use. We propose a new approach to rejection sampling by refining a parameterized proposal distribution with a loss derived from the acceptance threshold. In this manner we obtain comparable or better acceptance rates on current benchmarks by up to 7. 3×, while requiring no extra assumptions or any derivations to use: only a differentiable f(·) is required. While approximate, the results are correct with high probability, and in all tests pass a distributional check. This makes our approach easy to use, reproduce, and efficacious.

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AAAI Conference 2021 Conference Paper

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

Edward Raff
William Fleshman
Richard Zak
Hyrum S. Anderson
Bobby Filar
Mark McLean

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed 100 MB, which corresponds to a time series with T = 100, 000, 000 steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to T = 2, 000, 000 steps. The O(T) memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length T. This makes MalConv 116× more memory efficient, and up to 25. 8× faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the Mal- Conv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https: //github. com/ NeuromorphicComputationResearchProgram/MalConv2

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NeurIPS Conference 2021 Conference Paper

Learning with Holographic Reduced Representations

Ashwinkumar Ganesan
Hang Gao
Sunil Gandhi
Edward Raff
Tim Oates
James Holt
Mark McLean

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differential component of a deep learning architecture. HRRs today are not effective in a differential solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop a output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach.

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AAAI Conference 2020 Conference Paper

A New Burrows Wheeler Transform Markov Distance

Edward Raff
Charles Nicholas
Mark McLean

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classiﬁcation tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a ﬁxed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.

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