Titulo Estágio
Interpretability of Learning Models
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
CISUC
Enquadramento
Deep learning methods, as convolutional and recurrent neural networks, are becoming standard go-to algorithms for a wide range of applications.
However, applicability in several critical applications, e.g. public policy, security/safety systems, health diagnosis and fraud detection, has been faced with some hurdles do to lack of model interpretability.
Such systems suffer from interpretability issues and in this internship it is proposed to research and implement detection and interpretability mechanisms that can be applied.
Objetivo
In this internship the student should study, propose, implement, and test methods for detection, interpretability and trustworthiness in learning models.
To achieve this goal, the following objectives will be pursued:
- Study the state of the art
- Study the available frameworks for model development
- Define the case studies
- Define, implement, and fine tune the detection/interpretability architecture
- Propose and deploy test setup
Plano de Trabalhos - Semestre 1
- Literature review
- Identification and study of detection/interpretability mechanisms
- Identification and study of available frameworks
- Analyse and define scenarios
- Define the architecture of the system
- Start implementing the proposed approach
- Write intermediate report
Plano de Trabalhos - Semestre 2
- Implement the proposed solution and fine tune models
- Test and evaluate performance
- Write final report
Condições
This work should take place in the context of a research project funded by FCT in a CISUC lab. There is the possibility of a 6-month scholarship of 798 euros per month.
Observações
During the application phase, doubts related to this proposal, namely regarding the objectives and conditions, should be clarified with the advisors, by e-mail (catarina@dei.uc.pt) or a meeting to be arranged after a contact by email.
Orientador
Catarina Silva, Bernardete Ribeiro, Joana Costa
catarina@dei.uc.pt 📩