Parallelization of Sequential Pattern Sampling

Diop, Lamine; Ba, Cheikh Oumar

doi:10.1109/bigdata52589.2021.9672071

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…This modular design not only contributes to the model's scalability but also simplifies the addition of more layers, thereby facilitating the development of larger and more powerful models. The parallelization [38], combined with layer normalization and residual connections, contributes to more stable and faster training, especially for large models. In contrast, training deep RNNs can be a delicate task, requiring careful initialization and regularization to mitigate challenges related to the sequential nature of computation.…”

Section: Transformermentioning

confidence: 99%

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

Golda,

Mekonen,

Pandey

et al. 2024

IEEE Access

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Generative Artificial Intelligence (GAI) has sparked a transformative wave across various domains, including machine learning, healthcare, business, and entertainment, owing to its remarkable ability to generate lifelike data. This comprehensive survey offers a meticulous examination of the privacy and security challenges inherent to GAI. It provides five pivotal perspectives essential for a comprehensive understanding of these intricacies. The paper encompasses discussions on GAI architectures, diverse generative model types, practical applications, and recent advancements within the field. In addition, it highlights current security strategies and proposes sustainable solutions, emphasizing user, developer, institutional, and policymaker involvement.

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Section: Transformermentioning

confidence: 99%

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

Golda,

Mekonen,

Pandey

et al. 2024

IEEE Access

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Trie-based Output Space Itemset Sampling

Diop

Giacometti

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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Itemset mining methods are techniques to discover relevant patterns in transactional databases. The first approach, called constrained-based pattern mining, is based on exhaustive pattern mining techniques which consist in returning all itemsets that satisfy a given constraint. The main issues that hinder their efficiency are the pattern explosion and the difficulty for a user to set the threshold value. To solve this problem, methods that return the most interesting patterns, called top-k, are also proposed, but they tend to lack diversity, a challenging issue for interactive pattern mining. However, interactive pattern mining is based on fast methods that respond effectively to user demand. To overcome all these problems, output pattern sampling is proposed to draw quickly a set of interesting patterns while guaranteeing a good diversity. Pattern sampling techniques are probabilistic methods that aim to draw a set of interesting patterns where each pattern is drawn with a probability proportional to a given interestingness measure. Nowadays, there are several measures that a user can test when interacting with the same database. In that case, the system should last a few times to consider the new utility measures while guaranteeing an exact draw. So, the cost in time of utility change can be a real problem for output pattern sampling techniques in large databases. In addition, with the current sampling methods, it is necessary to store all data in memory and this storage is prohibitive for large data. To solve these problems, this paper deals with how to structure the data for output pattern sampling under length-based utility measures in large transactional databases. So, we revisit the trie structure initially proposed by D. Knuth to enrich it and then no longer have the need (i) to access the data to sample because the patterns will be directly taken from the enriched trie, (ii) to reprocess the entire dataset when utility changes. The computation of the value of a length-based utility measure is based on the lengths of all the patterns that are present in the database. So, we define a new structure of trie called trie of occurrences, built by our first algorithm TPSpace (Trie-based Pattern Space), which materializes all the occurrences of the patterns in the database. Diop et alThe data compression comes from a factorization of the information via the prefixes of the patterns. The particularly remarkable result is that, by definition, the trie of occurrences is the same for any length-based utility measure provided that the same values are kept for the minimum and maximum length constraints. We then describe TPSampling (Trie-based Pattern Sampling) which performs the sampling by drawing patterns according to a length-based utility measure from the trie of occurrences. This paper is completed by the complexity analysis in memory and in time of the method and experiments on benchmark datasets. TPSampling is competitive with the twostep approach to sample following a given interestingness measure but, as expec...

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Parallelization of Sequential Pattern Sampling

Cited by 2 publications

References 7 publications

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

Trie-based Output Space Itemset Sampling

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