Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Carvalho, Andre C. P. L. F."'
The adoption of Deep Learning algorithms in the medical imaging field is a prominent area of research, with high potential for advancing AI-based Computer-aided diagnosis (AI-CAD) solutions. However, current solutions face challenges due to a lack of
Externí odkaz:
http://arxiv.org/abs/2404.04736
Autor:
Menezes, Angelo G., Peterlevitz, Augusto J., Chinelatto, Mateus A., de Carvalho, André C. P. L. F.
Continual Object Detection is essential for enabling intelligent agents to interact proactively with humans in real-world settings. While parameter-isolation strategies have been extensively explored in the context of continual learning for classific
Externí odkaz:
http://arxiv.org/abs/2402.12624
A fundamental question on the use of ML models concerns the explanation of their predictions for increasing transparency in decision-making. Although several interpretability methods have emerged, some gaps regarding the reliability of their explanat
Externí odkaz:
http://arxiv.org/abs/2209.05371
Autor:
Baz, Adrian El, Ullah, Ihsan, Alcobaça, Edesio, Carvalho, André C. P. L. F., Chen, Hong, Ferreira, Fabio, Gouk, Henry, Guan, Chaoyu, Guyon, Isabelle, Hospedales, Timothy, Hu, Shell, Huisman, Mike, Hutter, Frank, Liu, Zhengying, Mohr, Felix, Öztürk, Ekrem, van Rijn, Jan N., Sun, Haozhe, Wang, Xin, Zhu, Wenwu
Publikováno v:
NeurIPS 2021 Competition and Demonstration Track, Dec 2021, On-line, United States
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are avai
Externí odkaz:
http://arxiv.org/abs/2206.08138
The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned. Its focus has been mainly on incremental classification tasks. We believe that research in continual object de
Externí odkaz:
http://arxiv.org/abs/2205.15445
Autor:
Castilho, Douglas, Souza, Tharsis T. P., Kang, Soong Moon, Gama, João, de Carvalho, André C. P. L. F.
We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of
Externí odkaz:
http://arxiv.org/abs/2110.11751
Universal identifiers and hashing have been widely adopted in computer science from distributed financial transactions to data science. This is a consequence of their capability to avoid many shortcomings of relative identifiers, such as limited scop
Externí odkaz:
http://arxiv.org/abs/2109.06028
Autor:
Mastelini, Saulo M., Cassar, Daniel R., Alcobaça, Edesio, Botari, Tiago, de Carvalho, André C. P. L. F., Zanotto, Edgar D.
Due to their unique optical and electronic functionalities, chalcogenide glasses are materials of choice for numerous microelectronic and photonic devices. However, to extend the range of compositions and applications, profound knowledge about compos
Externí odkaz:
http://arxiv.org/abs/2106.07749
Autor:
Basgalupp, Márcio P., Barros, Rodrigo C., de Sá, Alex G. C., Pappa, Gisele L., Mantovani, Rafael G., de Carvalho, André C. P. L. F., Freitas, Alex A.
This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on Evolutionary Algorithms (EAs), and
Externí odkaz:
http://arxiv.org/abs/2009.07430
Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine learning models have been proposed to address this problem. In this wo
Externí odkaz:
http://arxiv.org/abs/2009.05818