Antonio Tepsich
Estudiante de Ing. en Inteligencia Artificial (UdeSA) / Desarrollador FullStack.
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Telegram
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Twitter
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linkedIn
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GitHub
AntonioTepsich has 15 repositories available. Follow their code on GitHub.
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Instagram
Publicaciones Destacadas
Podio Hackathon AWS 🥉
Podio Hackathon AWS 🥉
Resumen 2023 🔙
Resumen 2023 🔙
Ultimos Eventos Organizados
Martin Migoya - CEO Globant
Martin Migoya - CEO Globant
Nahuel Lema - CoderHouse
Nahuel Lema - CoderHouse
Ayer fue un día muy especial para mí 💥 Hace dos años empecé mi camino como desarrollador, y hoy tuve el privilegio de recibir junto al Club de Emprendedores UdeSA a Nahuel Lema, Co-Founder de Coderhouse, la empresa que me ayudó a dar mis primeros pasos en el mundo de la programación 💻👣 Nahuel compartió su historia y los desafíos que enfrentó al construir Coderhouse, la plataforma educativa líder en el mercado. Sus experiencias nos brindaron insights únicos sobre el proceso y las decisiones que tuvo que tomar para lograr que CoderHouse sea lo que es hoy en día 🚀 Quiero agradecer especialmente a Nahuel por esta oportunidad, al equipo de Club de Emprendedores y a los mas de 100 estudiantes de Universidad de 'San Andrés' que vinieron a la charla!! 👏
Últimos proyectos
Mileipedia
Mileipedia
Primera herramienta sin afiliación política, diseñada para la búsqueda de discursos del presidente de la República Argentina, Javier Milei, y la Vocería Presidencial de Manuel Adorni
+670⭐GitHub - Convolutional Kolmogorov-Arnold Networks
+670⭐GitHub - Convolutional Kolmogorov-Arnold Networks
This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learna...
Problema del Viajero resuelto con Algoritmos Geneticos
Problema del Viajero resuelto con Algoritmos Geneticos
Travelling Salesman Problem solved with Genetic Algorithms - AntonioTepsich/Travelling-Salesman-Problem
Research paper
Paper - Convolutional Kolmogorov-Arnold Networks
Paper - Convolutional Kolmogorov-Arnold Networks
In this paper, we present Convolutional Kolmogorov-Arnold Networks, a novel architecture that integrates the learnable spline-based activation functions of Kolmogorov-Arnold Networks (KANs) into convolutional layers. By replacing traditional fixed-weight kernels with learnable non-linear functions, Convolutional KANs offer a significant improvement in parameter efficiency and expressive power over standard Convolutional Neural Networks (CNNs). We empirically evaluate Convolutional KANs on the Fashion-MNIST dataset, demonstrating competitive accuracy with up to 50% fewer parameters compared to baseline classic convolutions. This suggests that the KAN Convolution can effectively capture complex spatial relationships with fewer resources, offering a promising alternative for parameter-efficient deep learning models.
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