Browsing by Author "Hurtado, Julio"

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    Boosting SpLSA for Text Classification
    (2017) Hurtado, Julio; Mendoza, Marcelo; Nanculef, Ricardo
    Text classification is a challenge in document labeling tasks such as spam filtering and sentiment analysis. Due to the descriptive richness of generative approaches such as probabilistic Latent Semantic Analysis (pLSA), documents are often modeled using these kind of strategies. Recently, a supervised extension of pLSA (spLSA [10]) has been proposed for human action recognition in the context of computer vision. In this paper we propose to extend spLSA to be used in text classification. We do this by introducing two extensions in spLSA: (a) Regularized spLSA, and (b) Label uncertainty in spLSA. We evaluate the proposal in spam filtering and sentiment analysis classification tasks. Experimental results show that spLSA outperforms pLSA in both tasks. In addition, our extensions favor fast convergence suggesting that the use of spLSA may reduce training time while achieving the same accuracy as more expensive methods such as sLDA or SVM.
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    PIVOT: Prompting for Video Continual Learning
    (IEEE Computer Soc., 2023) Villa Ojeda, Andres Felipe; Alcazar, Juan Leon; Alfarra, Motasem; Alhamoud, Kumail; Hurtado, Julio; Heilbron, Fabian Caba; Soto, Alvaro; Ghanem, Bernard
    Modern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult or impossible to train and update large-scale models on such dynamic annotated sets. Continual learning directly approaches this problem, with the ultimate goal of devising methods where a deep neural network effectively learns relevant patterns for new (unseen) classes, without significantly altering its performance on previously learned ones. In this paper, we address the problem of continual learning for video data. We introduce PIVOT, a novel method that leverages extensive knowledge in pre-trained models from the image domain, thereby reducing the number of trainable parameters and the associated forgetting. Unlike previous methods, ours is the first approach that effectively uses prompting mechanisms for continual learning without any in-domain pre-training. Our experiments show that PIVOT improves state-of-the-art methods by a significant 27% on the 20-task ActivityNet setup.