Thanks to the state-of-the-art Large Language Models (LLMs), language generation has reached outstanding levels. These models are capable of generating high quality content, thus making it a challenging task to detect generated text from human-written content. Despite the advantages provided by Natural Language Generation, the inability to distinguish automatically generated text can raise ethical concerns in terms of authenticity. Consequently, it is important to design and develop methodologies to detect artificial content. In our work, we present some classification models constructed by ensembling transformer models such as Sci-BERT, DeBERTa and XLNet, with Convolutional Neural Networks (CNNs). Our experiments demonstrate that the considered ensemble architectures surpass the performance of the individual transformer models for classification. Furthermore, the proposed SciBERT-CNN ensemble model produced an F1-score of 98.36% on the ALTA shared task 2023 data.

Objective: In this work, we study the problem of cross-subject motor imagery (MI) decoding from electroenchephalography (EEG) data. Multi-subject EEG datasets present several kinds of domain shifts due to various inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and also impede robust cross-subject generalization. Method: We propose a two-stage model ensemble architecture, built with multiple feature extractors (first stage) and a shared classifier (second stage), which we train end-to-end with two loss terms. The first loss applies curriculum learning, forcing each feature extractor to specialize to a subset of the training subjects and promoting feature diversity. The second loss is an intra-ensemble distillation objective that allows collaborative exchange of knowledge between the models of the ensemble. Results: We compare our method against several state-of-the-art techniques, conducting subject-independent experiments on two large MI datasets, namely Physionet and OpenBMI. Our algorithm outperforms all of the methods in both 5-fold cross-validation and leave-one-subject-out evaluation settings, using a substantially lower number of trainable parameters. Conclusion: We demonstrate that our model ensembling approach combining the powers of curriculum learning and collaborative training, leads to high learning capacity and robust performance. Significance: Our work addresses the issue of domain shifts in multi-subject EEG datasets, paving the way for calibration-free BCI systems.

Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view cancer patient data. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our developed Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms.

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles"(Da San Martino et al., 2020). We participated in Task "Technique Classification" (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependentminority-class classifiers. A special classifier, which employs dynamically adapted Least Common Subsequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.

Yijun Bian 1, Qingquan Song 2, Mengnan Du 2, Jun Y ao 3, Huanhuan Chen 1, Xia Hu 2 1 University of Science and Technology of China 2 Department of Computer Science and Engineering, Texas A&M University 3 Data Science and Analytics Department, WeBank Abstract Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and the remarkable capability of reducing the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computation, which might not be affordable to researchers and practitioners. While recent attempts have employed ensemble learning methods to mitigate the enormous computation, an essential characteristic of diversity in ensemble methods is missed out, causing more similar sub-architectures to be gathered and potential redundancy in the final ensemble architecture. To bridge this gap, we propose a pruning method for NAS ensembles, named as " Sub-Architecture Ensemble Pruning in Neural Architecture Search (SAEP) ." It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures. Three possible solutions are proposed to decide which sub-architectures should be pruned during the searching process. Experimental results demonstrate the effectiveness of the proposed method in largely reducing the size of ensemble architectures while maintaining the final performance. Moreover, distinct deeper architectures could be discovered if the searched sub-architectures are not diverse enough. Introduction Designing neural network architectures usually requires manually elaborated architecture engineering, extensive expertise as well as expensive costs. Neural architecture search (NAS), which aims at mitigating these challenges, is attracting more and more attention recently (Zoph et al. 2018; Elsken, Metzen, and Hutter 2019; Wistuba, Rawat, and Pedapati 2019).