Examinando por Materia "Transformers"
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Publicación Asesoría y prospección de visitas de clientes en agencias de autos por medio de chatbots e Inteligencia Artificial(Universidad EAFIT, 2025) Restrepo Acosta, Eduardo; Martínez Vargas, Juan David; Sepúlveda Cano, Lina MaríaÍtem Cotton Price Long-Term Time Series Forecasting : A look at Transformers Suitability(Universidad EAFIT, 2024) Salazar Escobar, Carlos Enrique; Olarte, TomásRecent years have witnessed a surge of Transformer-based models for long-term time series forecasting (LTSF). These models boast impressive results in Natural Language Processing (NLP) and Computer Vision (CV), but their effectiveness in capturing the crucial temporal order inherent in time series data remains a question. This work investigates the suitability of Transformer-based models for long-term commodity price prediction, by replicating the work presented in "Are Transformers Effective for Time Series Forecasting?" by Zeng et al. (2022). We aim to evaluate their effectiveness compared to simpler baselines and analyze their limitations in capturing long-range dependencies. By delving deeper into these limitations, this research seeks to contribute to the development of more effective forecasting models for commodity price prediction.Publicación Detección automática de acordes empleando técnicas de caracterización de audio y machine learning(Universidad EAFIT, 2025) Gil Urrego, Rafael Alejandro; Martínez Vargas, Juan David; Sepúlveda Cano, Lina MaríaAutomatic chord detection in audio tracks is essential for developing various musical applications, such as music transcription and score generation. For this reason, there has been a growing interest in the field of data science to explore different strategies to address this need. The main approach studied in recent years is based on extracting features from audio files that contain chord information. Transforming the audio signal using different frequency analysis tools has generated data with a greater ability to describe the musical components present in the processed audio track. The Mel spectrogram and the Chromagram are some of the methods used for these tasks. Additionally, classical supervised analytical models such as Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNN) have been employed in several studies. These models have demonstrated a high level of accuracy in chord identification. However, in most cases, they have been limited by the number of chord classes to estimate, as an increase in the number of classes can confuse the system, typically allowing a maximum of 24. In this thesis, a system for automatic chord identification was developed by implementing different classical and modern analytical models. For audio feature extraction, the pre-trained models HuBERT and VGGish were used. These extracted features were then fed into three classical models—SVM, Random Forest, and Gradient Boosting—to compare their results with those obtained by a modern model. The HuBERT architecture was chosen as the modern baseline model since it can function both as a feature extractor and a classifier. The experiments were conducted using recordings of 48 different chord classes, all played on a digital piano, providing a solid dataset for training and evaluating the proposed system’s performance. The study confirmed previous research findings: to obtain accurate chord class estimations, it is crucial to improve the characterization techniques of the input audio recordings. A recurring issue identified was the lack of a detailed description of the musical components in the recordings, which affected the models’ ability to deliver optimal results. Our findings highlight that precise feature extraction is key to reducing model generalization error, enabling better chord class identification in both classical supervised approaches and modern architectures such as HuBERT. Finally, it is concluded that modern models, including those based on Transformers, have a high dependency on the quantity and diversity of the data. To achieve effective adaptability, the training data must exhibit sufficient variations within the same class. When data lack intra-class variability, these systems struggle to adapt to new recordings, especially those with background noise or distortions.Publicación Evaluación de rendimiento de diferentes modelo grandes de lenguaje para el reconocimiento de emociones en texto(Universidad EAFIT, 2024) López Atehortúa, David Alejandro; Montoya Múnera, Edwin NelsonIt is becoming more common for people to express their opinions in short texts through different media thanks to the expansion of internet access. Understanding and efficiently analyzing an individual’s sentiment from a text is a task that is useful in multiple scenarios. For the above, a branch of computer science called Natural Language Processing (NLP) has been dedicated to developing techniques to understand everything related to human language. Traditional techniques, based on the frequency of a word or a group of consecutive words to classify the text in a positive, negative or neutral sentiment. These techniques have limitations because they fail to capture the full context of each word in a sentence, affecting their accuracy and ability to detect a more detailed spectrum of emotions. Recently, Long Language Models (LLMs) or Transformers revolutionized the way NLP is performed thanks to their ability to capture the context around each word in a text. This allows for the detection of feelings in a more precise way and even, the classification of the text into a more specific emotion such as joy, optimism, anger, sadness or others. This project aims to evaluate the performance of different LLMs to find the best performing one in emotion detection from short texts in English using datasets typically used in research related to NLP models.Ítem Identificación de patrones socioeconómicos en Medellín a partir de imágenes satelitales(Universidad EAFIT, 2024) Ceballos Betancur, Mariana; Martínez Vargas, Juan David; Torres Madronero, María ConstanzaÍtem Reconocimiento de emociones a partir del Speech (SER)(Universidad EAFIT, 2023) Giraldo Toro, Jeison Erley; Montoya Múnera, Edwin Nelson; Martínez Vargas, Juan DavidÍtem Selección de sentimiento y tópicos a través de Transformers(Universidad EAFIT, 2023) Rendón Jiménez, Alejandro; Hernández Torres, Santiago