Zobrazeno 1 - 10
of 31 462
pro vyhledávání: '"TELLO, A."'
Autor:
Bovenzi, Inko, Carmel, Adi, Hu, Michael, Hurwitz, Rebecca M., McBride, Fiona, Benac, Leo, Ayala, José Roberto Tello, Doshi-Velez, Finale
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actio
Externí odkaz:
http://arxiv.org/abs/2411.05237
Autor:
Kumar, Shashi, Thorbecke, Iuliia, Burdisso, Sergio, Villatoro-Tello, Esaú, E, Manjunath K, Hacioğlu, Kadri, Rangappa, Pradeep, Motlicek, Petr, Ganapathiraju, Aravind, Stolcke, Andreas
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whe
Externí odkaz:
http://arxiv.org/abs/2411.03866
Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors lik
Externí odkaz:
http://arxiv.org/abs/2410.17655
Autor:
Bartolome, Macavilca Tello, Fernandez, Kevin, Cutipa-Luque, Oscar, Tiahuallpa, Yhon, Rojas, Helder
This study explores the dynamic relationship between corruption and economic growth through an approach based on a system of stochastic equations. In the context of globalization and economic interdependencies, corruption not only affects investment
Externí odkaz:
http://arxiv.org/abs/2410.08132
We investigate the cosmic evolution of the Universe across different cosmological epochs in exponential Weyl-type $f(Q, T)$ gravity model. The theoretical analysis involves a detailed dynamical system approach, where we define dimensionless variables
Externí odkaz:
http://arxiv.org/abs/2409.17193
Autor:
Thorbecke, Iuliia, Zuluaga-Gomez, Juan, Villatoro-Tello, Esaú, Carofilis, Andres, Kumar, Shashi, Motlicek, Petr, Pandia, Karthik, Ganapathiraju, Aravind
Despite the recent success of end-to-end models for automatic speech recognition, recognizing special rare and out-of-vocabulary words, as well as fast domain adaptation with text, are still challenging. It often happens that biasing to the special e
Externí odkaz:
http://arxiv.org/abs/2409.13514
Autor:
Thorbecke, Iuliia, Zuluaga-Gomez, Juan, Villatoro-Tello, Esaú, Kumar, Shashi, Rangappa, Pradeep, Burdisso, Sergio, Motlicek, Petr, Pandia, Karthik, Ganapathiraju, Aravind
The training of automatic speech recognition (ASR) with little to no supervised data remains an open question. In this work, we demonstrate that streaming Transformer-Transducer (TT) models can be trained from scratch in consumer and accessible GPUs
Externí odkaz:
http://arxiv.org/abs/2409.13499
In this paper we study the existence of solutions of a parabolic-elliptic system of partial differential equations describing the behaviour of a biological species $u$ and a chemical stimulus $v$ in a bounded and regular domain $\Omega$ of $\mathbb{R
Externí odkaz:
http://arxiv.org/abs/2409.10121
Autor:
Kich, Victor Augusto, Kolling, Alisson Henrique, de Jesus, Junior Costa, Heisler, Gabriel V., Jacobs, Hiago, Bottega, Jair Augusto, Kelbouscas, André L. da S., Ohya, Akihisa, Grando, Ricardo Bedin, Drews-Jr, Paulo Lilles Jorge, Gamarra, Daniel Fernando Tello
This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the ta
Externí odkaz:
http://arxiv.org/abs/2408.05744
Autor:
Hernández-Tello, Javier, Martínez-del-Amor, Miguel Ángel, Orellana-Martín, David, Cabarle, Francis George C.
Publikováno v:
International Journal of Neural Systems, Vol. 34, No. 07 (2024), 2450038
The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which
Externí odkaz:
http://arxiv.org/abs/2408.04343