Supervised Shallow Multi-task Learning: Analysis of Methods

Abhadiomhen, Stanley Ebhohimhen; Nzeh, Royransom; Ganaa, Ernest Domanaanmwi; Nwagwu, Honour Chika; Okereke, George; Routray, Sidheswar

doi:10.1007/s11063-021-10703-7

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…e subproblems of F can be solved by solving the characteristic equations. For the S subproblem, the SVT [33,34] method is used to solve it. For the E subproblem, the effectiveness and convergence of the solution have also been confirmed [35].…”

Section: Convergence Studymentioning

confidence: 99%

Robust Spectral Clustering via Low-Rank Sample Representation

Liang

Guan²,

Abhadiomhen

et al. 2022

Applied Computational Intelligence and Soft Computing

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Traditional clustering methods neglect the data quality and perform clustering directly on the original data. Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In order to resolve the previously mentioned problem, a new method is proposed, which builds on the approach of low-rank representation. The proposed approach first learns a low-rank coefficient matrix from data by exploiting the data’s self-expressiveness property. Then, a regularization term is introduced to ensure that the representation coefficient of two samples, which are similar in original high-dimensional space, is close to maintaining the samples’ neighborhood structure in the low-dimensional space. As a result, the proposed method obtains a clustering structure directly through the low-rank coefficient matrix to guarantee optimal clustering performance. A wide range of experiments shows that the proposed method is superior to compared state-of-the-art methods.

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Section: Convergence Studymentioning

confidence: 99%

Robust Spectral Clustering via Low-Rank Sample Representation

Liang

Guan²,

Abhadiomhen

et al. 2022

Applied Computational Intelligence and Soft Computing

Self Cite

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“…Many application domains were studied in previous work, ranging from surveys covering multiple domains (Ruder, 2017;Zhang & Yang, 2017;Thung & Wee, 2018;Vafaeikia et al, 2020;Crawshaw, 2020;Upadhyay et al, 2021;Abhadiomhen et al, 2022), to those dedicated to a specific domain, such as computer vision (Vandenhende et al, 2021) or natural language processing (Zhou, 2019;Worsham & Kalita, 2020;Samant et al, 2022;Zhang et al, 2023). Both traditional ML and deep learning computational models were studied.…”

mentioning

confidence: 99%

Speeding Up Transformer Training By Using Dataset Subsampling - An Exploratory Analysis

Torbarina¹,

Mihelčić²,

Šarlija³

et al. 2021

Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing

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Transformer-based models have greatly advanced the progress in the field of the natural language processing and while they achieve state-of-the-art results on a wide range of tasks, they are cumbersome in parameter size. Subsequently, even when pre-trained transformer models are used for fine-tuning on a given task, if the dataset is large, it may still not be feasible to fine-tune the model within a reasonable time. For this reason, we empirically test 8 subsampling methods for reducing the dataset size on text classification task and report the trade-off between metric score and training time. 7 out of 8 methods are simple methods, while the last one is CRAIG, a method for coreset construction for data-efficient model training. We obtain the best result with the CRAIG method, offering an average decrease of 0.03 points in f-score on test set while speeding up the training time on average by 63.93%, relative to the score and time obtained by using the full dataset. Lastly, we show the trade-off between speed and performance for all sampling methods on three different datasets.

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