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ORCIDGabriel de Oliveira Ramos
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2020 – today
- 2024
[j19]Vitor A. Fraga, Lincoln Vinicius Schreiber, Marco Antonio Chitolina Da Silva, Rafael Kunst
, Jorge L. V. Barbosa, Gabriel de Oliveira Ramos:
A machine learning pipeline for extracting decision-support features from traffic scenes. AI Commun. 37(2): 189-201 (2024)- [j18]Felipe André Zeiser, Cristiano André da Costa, Gabriel de Oliveira Ramos, Andreas K. Maier, Rodrigo da Rosa Righi:
CheXReport: A transformer-based architecture to generate chest X-ray reports suggestions. Expert Syst. Appl. 255: 124644 (2024) - [j17]Gefersom Lima, Felipe André Zeiser, Ariane da Silveira, Sandro José Rigo, Gabriel de Oliveira Ramos:
An encoder-decoder deep neural network for binary segmentation of seismic facies. Comput. Geosci. 183: 105507 (2024) - [j16]Luis Antonio Leite Francisco da Costa, Mateus Begnini Melchiades, Valéria Soldera Girelli, Felipe Colombelli, Denis Andrei de Araújo, Sandro José Rigo, Gabriel de Oliveira Ramos, Cristiano André da Costa, Rodrigo da Rosa Righi, Jorge Luis Victória Barbosa:
Advancing Chatbot Conversations: A Review of Knowledge Update Approaches. J. Braz. Comput. Soc. 30(1): 55-68 (2024) - [c30]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. AAMAS 2024: 2717-2721 - [i7]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. CoRR abs/2402.02665 (2024) - 2023
- [j15]Juarez Machado da Silva, Gabriel de Oliveira Ramos, Jorge Luis Victória Barbosa:
Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects. Algorithms 16(3): 162 (2023) - [j14]Marcos Leandro Hoffmann Souza, Cristiano André da Costa, Gabriel de Oliveira Ramos:
A machine-learning based data-oriented pipeline for Prognosis and Health Management Systems. Comput. Ind. 148: 103903 (2023) - [c29]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Friedrik Hentz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar Reward is Not Enough. AAMAS 2023: 839-841 - [c28]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c27]Felipe André Zeiser, Ismael Santos, Henrique Bohn, Cristiano André da Costa, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi, Andreas Maier, José Rodrigo M. Andrade, Alexandre Bacelar:
Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning. WEBIST 2023: 399-405 - 2022
- [j13]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j12]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Fredrik Heintz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). Auton. Agents Multi Agent Syst. 36(2): 41 (2022) - [j11]Tiago Boechel, Lucas Micol Policarpo, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi, Dhananjay Singh:
Prediction of Harvest Time of Apple Trees: An RNN-Based Approach. Algorithms 15(3): 95 (2022) - [j10]Luana Carine Schünke, Blanda Mello, Cristiano André da Costa, Rodolfo Stoffel Antunes, Sandro José Rigo, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi, Juliana Nichterwitz Scherer, Bruna Donida:
A rapid review of machine learning approaches for telemedicine in the scope of COVID-19. Artif. Intell. Medicine 129: 102312 (2022) - [j9]Jovani Dalzochio, Rafael Kunst, Jorge Luis Victória Barbosa, Henrique Damasceno Vianna, Gabriel de Oliveira Ramos, Edison Pignaton, Alécio P. D. Binotto, Jose Favilla:
ELFpm: A machine learning framework for industrial machines prediction of remaining useful life. Neurocomputing 512: 420-442 (2022) - [c26]Igor Felipe de Camargo, Rodolfo Stoffel Antunes, Gabriel de Oliveira Ramos:
On Social Consensus Mechanisms for Federated Learning Aggregation. BRACIS (2) 2022: 236-250 - [c25]Lucas M. Ceschini, Lucas Micol Policarpo, Rodrigo da Rosa Righi, Gabriel de Oliveira Ramos:
Aiding Glaucoma Diagnosis from the Automated Classification and Segmentation of Fundus Images. BRACIS (2) 2022: 343-356 - [c24]Felipe Colombelli, Vítor Kehl Matter, Bruno Iochins Grisci, Leomar Lima, Karine Heinen, Marcio Borges, Sandro José Rigo, Jorge Luis Victória Barbosa, Rodrigo da Rosa Righi, Cristiano André da Costa, Gabriel de Oliveira Ramos:
Multi-objective prioritization for data center vulnerability remediation. CEC 2022: 1-8 - [c23]João Batista Rodrigues Neto, Gabriel de Oliveira Ramos:
An Interpolated Approach for Active Debris Removal. CEC 2022: 1-6 - [c22]Juarez Machado da Silva, Gabriel de Oliveira Ramos, Jorge L. V. Barbosa:
The multi-objective dynamic shortest path problem. CEC 2022: 1-8 - [c21]Vitor A. Fraga, Lincoln Schreiber, Rafael Kunst, Jorge L. V. Barbosa, Gabriel de Oliveira Ramos:
A Machine Learning Pipeline for Extracting Decision-Support Features from Traffic Scenes. ATT@IJCAI 2022: 194-208 - [c20]Samuel A. Freitas, Felipe André Zeiser, Cristiano André da Costa, Gabriel de Oliveira Ramos:
DeepCADD: A Deep Learning Architecture for Automatic Detection of Coronary Artery Disease. IJCNN 2022: 1-8 - [c19]Lincoln Vinicius Schreiber, Lucas Nunes Alegre, Ana L. C. Bazzan, Gabriel de Oliveira Ramos:
On the Explainability and Expressiveness of Function Approximation Methods in RL-Based Traffic Signal Control. IJCNN 2022: 1-8 - [c18]Arturo de Souza, Mateus Begnini Melchiades, Sandro José Rigo, Gabriel de Oliveira Ramos:
MoStress: a Sequence Model for Stress Classification. IJCNN 2022: 1-8 - [i6]Bruno Grisci, Gabriela Kuhn, Felipe Colombelli, Vítor Kehl Matter, Leomar Lima, Karine Heinen, Mauricio Pegoraro, Marcio Borges, Sandro José Rigo, Jorge L. V. Barbosa, Rodrigo da Rosa Righi, Cristiano André da Costa, Gabriel de Oliveira Ramos:
Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimization. CoRR abs/2202.07466 (2022) - 2021
- [j8]Miromar José de Lima, César David Paredes Crovato, Rodrigo Ivan Goytia Mejia, Rodrigo da Rosa Righi, Gabriel de Oliveira Ramos, Cristiano André da Costa, Giovani Pesenti:
HealthMon: An approach for monitoring machines degradation using time-series decomposition, clustering, and metaheuristics. Comput. Ind. Eng. 162: 107709 (2021) - [j7]Marcos Leandro Hoffmann Souza, Cristiano André da Costa, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi:
A feature identification method to explain anomalies in condition monitoring. Comput. Ind. 133: 103528 (2021) - [j6]Felipe André Zeiser, Cristiano André da Costa, Gabriel de Oliveira Ramos, Henrique Bohn, Ismael Santos, Adriana Vial Roehe:
DeepBatch: A hybrid deep learning model for interpretable diagnosis of breast cancer in whole-slide images. Expert Syst. Appl. 185: 115586 (2021) - [j5]Nícolas B. Santos, Rodrigo Simon Bavaresco, João Elison da Rosa Tavares, Gabriel de Oliveira Ramos, Jorge L. V. Barbosa:
A systematic mapping study of robotics in human care. Robotics Auton. Syst. 144: 103833 (2021) - [c17]Felipe André Zeiser, Cristiano André da Costa, Gabriel de Oliveira Ramos, Henrique Bohn, Ismael Santos, Rodrigo da Rosa Righi:
Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays. BRACIS (2) 2021: 121-132 - [c16]João Batista Rodrigues Neto, Gabriel de Oliveira Ramos:
An Enhanced TSP-Based Approach for Active Debris Removal Mission Planning. BRACIS (1) 2021: 140-154 - [c15]Tiago Boechel, Lucas Micol Policarpo, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi:
Fuzzy time series for predicting phenological stages of apple trees. SAC 2021: 934-941 - [i5]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i4]Felipe André Zeiser, Cristiano André da Costa, Gabriel de Oliveira Ramos, Henrique Bohn, Ismael Santos, Rodrigo da Rosa Righi:
Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays. CoRR abs/2109.02415 (2021) - [i3]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Fredrik Heintz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021). CoRR abs/2112.15422 (2021) - 2020
- [j4]Gabriel Souto Fischer, Rodrigo da Rosa Righi, Gabriel de Oliveira Ramos, Cristiano André da Costa, Joel J. P. C. Rodrigues:
ElHealth: Using Internet of Things and data prediction for elastic management of human resources in smart hospitals. Eng. Appl. Artif. Intell. 87 (2020) - [j3]Gabriel de Oliveira Ramos, Bruno C. da Silva, Roxana Radulescu, Ana L. C. Bazzan, Ann Nowé:
Toll-based reinforcement learning for efficient equilibria in route choice. Knowl. Eng. Rev. 35: e8 (2020) - [c14]Gabriel de Oliveira Ramos, Roxana Radulescu, Ann Nowé, Anderson R. Tavares:
Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences. AAMAS 2020: 1098-1106 - [c13]Ricardo Grunitzki, Gabriel de Oliveira Ramos:
On the Role of Reward Functions for Reinforcement Learning in the Traffic Assignment Problem. IJCNN 2020: 1-9 - [i2]Gefersom Lima, Gabriel de Oliveira Ramos, Sandro Rigo, Felipe André Zeiser, Ariane da Silveira:
Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks. CoRR abs/2012.03675 (2020)
2010 – 2019
- 2019
- [c12]Bruno Klein Salvalaio, Gabriel de Oliveira Ramos:
Self-Adaptive Appearance-Based Eye-Tracking with Online Transfer Learning. BRACIS 2019: 383-388 - [c11]Lucas Oliveira Souza, Gabriel de Oliveira Ramos, Célia Ghedini Ralha:
Experience Sharing Between Cooperative Reinforcement Learning Agents. ICTAI 2019: 963-970 - [i1]Lucas Oliveira Souza, Gabriel de Oliveira Ramos, Célia Ghedini Ralha:
Experience Sharing Between Cooperative Reinforcement Learning Agents. CoRR abs/1911.02191 (2019) - 2017
- [c10]Gabriel de Oliveira Ramos, Bruno Castro da Silva, Ana L. C. Bazzan:
Learning to Minimise Regret in Route Choice. AAMAS 2017: 846-855 - [c9]Gabriel de Oliveira Ramos:
Minimising Regret in Route Choice. AAMAS 2017: 1855-1856 - 2016
- [c8]Gabriel de Oliveira Ramos, Ana L. C. Bazzan:
Efficient local search in traffic assignment. CEC 2016: 1493-1500 - [c7]Gabriel de Oliveira Ramos, Ana L. C. Bazzan:
On Estimating Action Regret and Learning From It in Route Choice. ATT@IJCAI 2016 - 2015
- [j2]Gabriel de Oliveira Ramos, Juan C. Burguillo, Ana L. C. Bazzan:
A self-adapting similarity-based coalition formation approach for plug-in electric vehicles in smart grids. Multiagent Grid Syst. 11(3): 167-187 (2015) - [c6]Gabriel de Oliveira Ramos, Ana Lúcia Cetertich Bazzan:
Towards the User Equilibrium in Traffic Assignment Using GRASP with Path Relinking. GECCO 2015: 473-480 - [c5]Ana L. C. Bazzan, Gabriel de Oliveira Ramos:
Forming Coalitions of Electric Vehicles in Constrained Scenarios. PAAMS (Workshops) 2015: 237-248 - 2014
- [j1]Gabriel de Oliveira Ramos, Juan C. Burguillo, Ana L. C. Bazzan:
Dynamic constrained coalition formation among electric vehicles. J. Braz. Comput. Soc. 20(1): 8:1-8:15 (2014) - [c4]Gabriel de Oliveira Ramos, Ricardo Grunitzki:
An Improved Learning Automata Approach for the Route Choice Problem. ARE/AVSA@AAMAS 2014: 56-67 - [c3]Ricardo Grunitzki, Gabriel de Oliveira Ramos, Ana Lúcia C. Bazzan:
Individual versus Difference Rewards on Reinforcement Learning for Route Choice. BRACIS 2014: 253-258 - 2013
- [c2]Andrew Koster, Gabriel de Oliveira Ramos, Ana L. C. Bazzan, Fernando Koch:
Towards a Platform for Testing and Developing Privacy-Preserving Data Mining Applications for Smart Grids. MATES 2013: 292-305 - [c1]Gabriel de Oliveira Ramos, Juan C. Burguillo-Rial, Ana L. C. Bazzan:
Self-Adapting Coalition Formation Among Electric Vehicles in Smart Grids. SASO 2013: 11-20
Coauthor Index
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last updated on 2024-08-05 21:22 CEST by the dblp team
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