Transformer models as an efficient replacement for statistical test suites to evaluate the quality of random numbers

• URL: http://arxiv.org/abs/2405.03904v2
• Date: Wed, 23 Oct 2024 20:18:14 GMT
• Title: Transformer models as an efficient replacement for statistical test suites to evaluate the quality of random numbers
• Authors: Rishabh Goel, YiZi Xiao, Ramin Ramezani,
• Abstract summary: We present a deep learning model that performs multiple NIST STS tests at once and runs much faster. This model outputs multi-label classification results on passing these statistical tests. We also compared this model to a conventional deep learning method to quantify randomness and showed our model achieved similar performances.
• Score: 0.0
• License:
• Abstract: Random numbers are incredibly important in a variety of fields, and the need for their validation remains important for safety. A Quantum Random Number Generator (QRNG) can theoretically generate truly random numbers, however their quality still needs to be thoroughly validated. Generally, the task of validating random numbers has been delegated to different statistical tests such as the tests from the NIST Statistical Test Suite (STS), which are often slow and only perform one test at a time. Our work presents a deep learning model utilizing the Transformer architecture that 1) performs multiple NIST STS tests at once, and 2) runs much faster. This model outputs multi-label classification results on passing these statistical tests. We performed a thorough hyper-parameter optimization to converge on the best possible model and as a result, achieved a high degree of accuracy with a Macro F1-score of above 0.96. We also compared this model to a conventional deep learning method (Long Short Term Memory Recurrent Neural Networks) to quantify randomness and showed our model achieved similar performances while being much more efficient and scalable. The high performance and efficiency of this Transformer-based deep learning model showed that it can be a viable replacement for the NIST STS for validating random numbers.

Related papers

• On Using Quasirandom Sequences in Machine Learning for Model Weight Initialization [0.0]
  We investigate whether substituting PRNGs for low-discrepancy quasirandom number generators (QRNGs) as a source of randomness for initializers can improve model performance. Our findings indicate that QRNG-based neural network initializers either reach a higher accuracy or achieve the same accuracy more quickly than PRNG-based initializers.
  arXiv  Detail & Related papers  (2024-08-05T17:33:09Z)
• Statistical testing of random number generators and their improvement using randomness extraction [0.0]
  We present and make available a comprehensive statistical testing environment (STE) based on existing statistical test suites. The STE can be parameterised to run lightweight (i.e. fast) all the way to intensive testing, which goes far beyond what is required by certification bodies. We then present and implement a variety of post-processing methods, in the form of randomness extractors, which improve the RNG's output quality under different sets of assumptions.
  arXiv  Detail & Related papers  (2024-03-27T16:05:02Z)
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks [65.34977803841007]
  Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. We show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one.
  arXiv  Detail & Related papers  (2022-11-16T00:11:04Z)
• Validation tests of GBS quantum computers give evidence for quantum advantage with a decoherent target [62.997667081978825]
  We use positive-P phase-space simulations of grouped count probabilities as a fingerprint for verifying multi-mode data. We show how one can disprove faked data, and apply this to a classical count algorithm.
  arXiv  Detail & Related papers  (2022-11-07T12:00:45Z)
• Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control [11.011242089340438]
  In high dimensional variable selection problems, statisticians often seek to design multiple testing procedures that control the False Discovery Rate (FDR) We introduce Model-X procedures that provably control the frequentist FDR from finite samples, even when the model is misspecified. Our proposed procedure, PoEdCe, incorporates three key ingredients: Posterior Expectation, distilled randomization test (dCRT), and the Benjamini-Hochberg procedure with e-values.
  arXiv  Detail & Related papers  (2022-11-04T22:56:41Z)
• Learning to Increase the Power of Conditional Randomization Tests [8.883733362171032]
  The model-X conditional randomization test is a generic framework for conditional independence testing. We introduce novel model-fitting schemes that are designed to explicitly improve the power of model-X tests.
  arXiv  Detail & Related papers  (2022-07-03T12:29:25Z)
• Quantifying Inherent Randomness in Machine Learning Algorithms [7.591218883378448]
  This paper uses an empirical study to examine the effects of randomness in model training and randomness in the partitioning of a dataset into training and test subsets. We quantify and compare the magnitude of the variation in predictive performance for the following ML algorithms: Random Forests (RFs), Gradient Boosting Machines (GBMs), and Feedforward Neural Networks (FFNNs)
  arXiv  Detail & Related papers  (2022-06-24T15:49:52Z)
• TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision [70.05605071885914]
  We propose a novel modification of the self-supervised training algorithm SwAV that adds the ability to adapt to single test samples. We show the success of our method on the common benchmark dataset CIFAR10-C.
  arXiv  Detail & Related papers  (2022-05-18T05:43:06Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.