Accurate prediction of stroke outcome from computed
tomography scansSupervised by Arlindo L. OliveiraAccurately predicting the functional outcome of stroke
patients remains a problem with medical relevance that cannot
yet be adequately solved by automated means. Although it is
known that brain computed tomography (CT) scans contain
relevant information, their practical usefulness in predicting
this variable remains an open question. The objective of this
thesis is to develop algorithms to determine whether brain CT
scans (with and without contrast) could be automatically
analysed using deep learning models, to improve the prediction
of the three months post stroke functional outcome, as
measured by the modified ranking score. The selected student
will study the application of deep learning architectures,
including convolutional neural networks and vision
transformers, to the this problem. One intermediate variable
that will be studied as a possible predictor for the
functional outcome is occlusion, the existence of blocked
arteries that lead to the death of brain regions. Requisites:
The student should have significant programming experience,
and practical knowledge of machine learning languages and
environments, such as PyTorch or TensorFlow. The student
should have an interest in becoming familiar with the
biological and medical phenomena involved in stroke. Notes:
This work will be developed in close cooperation with the
neurology department of the Santa Maria Hospital. The selected
student will have access to the facilities of INESC-ID and the
MLKD group (https://mlkd.idss.inesc-id.pt/), including
computing facilities that include four DELL PowerEdge C41402
servers, eight NVIDIA 32GB Tesla V100S and eight NVIDIA 64GB
Tesla A100, among other computing servers.
Artificial Intelligence driven Document Workflow Systems: A
Framework for Automation and EfficiencySupervised by Arlindo L. Oliveira, Filipe Correia and Miguel
FreireIn today's fast-paced business environment, document-based
workflows play a crucial role in various organizational
processes. However, the manual processing of documents can be
time-consuming, error-prone, and resource-intensive. This
thesis proposes a framework for intelligent processing based
on document information and metadata in workflow solutions,
aiming to automate document processing and enhance efficiency.
The thesis will develop deep learning techniques to perform
natural language processing (NLP) and automated document
classification, routing, task assignment, and decision support
within the workflow. The workflow should be adaptable for
multiple domains without extensive training. The expected
result of this thesis is a deep learning framework to automate
and streamline business processes reducing manual tasks, human
errors, and delays caused by paper-based and non-automated
document processing. Notes: This work will be developed in
cooperation with Link Consulting and NeuralShift. The selected
student will have access to the facilities of INESC-ID and the
MLKD group (https://mlkd.idss.inesc-id.pt/), including
computing facilities that include four DELL PowerEdge C41402
servers, eight NVIDIA 32GB Tesla V100S and eight NVIDIA 64GB
Tesla A100, among other computing servers
(https://mlkd.idss.inesc-id.pt/cluster)
Using large language models to interact with personal
information systemsSupervised by Arlindo L. OliveiraLarge language models, such as ChatGPT and GPT-4 have shown
remarkable abilities to interact in natural language. However,
they cannot be used to access and learn from personal data,
stored in email records, note taking systems or photos and
videos. The objective of this dissertation is to design a
system that uses large language model as the interface for
personal data, using APIs and enabling the user to query,
relate and retrieve information stored in different
sub-systems, such as mailboxes, Google records and note taking
platforms such as Obsidian. The resulting system should be
able to emulate the behavior of an intelligent assistant that
has access to all stored personal data and, ultimately, to
answer questions about that data in a way similar to the user
that owns the data. Requisites: The student should have
significant programming experience, and practical knowledge of
machine learning languages and environments, such as PyTorch
or TensorFlow. He/she should also have interest in developing
the understanding of large language models and LLM APIs.
Notes: The selected student will have access to the facilities
of INESC-ID and the MLKD group
(https://mlkd.idss.inesc-id.pt/), including computing
facilities that include four DELL PowerEdge C41402 servers,
eight NVIDIA 32GB Tesla V100S and eight NVIDIA 64GB Tesla
A100, among other computing servers
(https://mlkd.idss.inesc-id.pt/cluster) Modelos de causalidade para determinação do impacto de acções
comerciaisSupervised by Arlindo L. Oliveira and Filipa MarquesNesta tese, irão ser desenvolvidos modelos de causalidade
para quantificar o impacto comercial das diferentes acções que
resultam do processo de geração de leads pelos modelos
analíticos. O objectivo é quantificar o impacto dos modelos de
análise de dados no negócio, determinando quais acções
específicas tiveram impacto no resultado final, usando para
tal modelos de causalidade. Serão usados dados reais de
cliente e de histórico de vendas da Fidelidade e analisadas as
consequências da diferentes acções de geração de leads a
partir da analítica. Serão também desenvolvidas métricas para
avaliar o impacto dos modelos de scores no resultado final do
negócio. Requisitos: o candidato deverá ter conhecimentos e
interesse em análise de dados, aprendizagem automática e
mecanismos de causalidade. A frequência de disciplinas destas
áreas é recomendada. Notas: Esta tese será desenvolvida em
parceria com a Fidelidade e co-orientada pela Dra. Filipa
Marques. Characterization of the internal representations of Large
Language ModelsSupervised by Arlindo L. Oliveira and Bruno MartinsLarge language models, mainly based on the transformer
architecture, trained in extensive amounts of data, have shown
a surprising ability to perform many different tasks with
little specific training. In particular, some of these models
exhibited an ability to perform few shot learning and even
zero shot learning. Reports on the emerging abilities of large
language models have also presented evidence that these models
are able to significantly extrapolate from the training data
used and solve problems in which they were not supposed to be
proficient. This dissertation will explore the internal
representations or large language models, in order to
characterize the nature of these representations and shed
light on the mechanisms used to represent knowledge. The work
to be developed will use publicly available models, such as
LLaMA and signal processing techniques inspired in the ones
used to process biological signals in the cortex of mammals to
explore and characterize the internal representations of large
language models, with the objective of shedding light on the
way these models work and, possibly, on how they are able to
generalize from training data to new domains. Requisites: The
student should have significant programming experience, and
practical knowledge of machine learning languages and
environments, such as PyTorch or TensorFlow. He/she should
also have interest in developing the understanding of large
language models and LLM APIs. Notes: The selected student will
have access to the facilities of INESC-ID and the MLKD group
(https://mlkd.idss.inesc-id.pt/), including computing
facilities that include four DELL PowerEdge C41402 servers,
eight NVIDIA 32GB Tesla V100S and eight NVIDIA 64GB Tesla
A100, among other computing servers
(https://mlkd.idss.inesc-id.pt/cluster) Using biological features to improve deep neural network
models for visionSupervised by Arlindo L. Oliveira and Tiago MarquesConvolutional neural networks and vision transformers
represent the state of the art in artificial neural network
(ANN) models for vision problems, such as classification,
segmentation, and object detection. However, the performance
of these models still falls behind human performance in many
problems and is highly susceptible to image variation,
lighting conditions, and deliberate attacks. Recent results
have shown that it is possible to draw inspiration from the
architecture and function of the visual cortex to improve the
performance of ANNs and to make these systems more robust to a
wide range of image perturbations. The objective of this
dissertation is to study how structural and functional
characteristics of the primate visual pathways can be used to
derive new layers and optimization goals in deep neural
networks that contribute to improving their robustness and
performance in image classification tasks. Efficient coding
algorithms, used in the retina and the primary visual cortex,
and different connection patterns between layers are some of
the approaches that will be tested. The novel models will be
assessed both in terms of their performance in existing
computer vision benchmarks and on how well their internal
components and behavioral output match those of real primate
brains using the Brain-Score platform. The work will be
co-supervised by Tiago Marques, currently at the Champalimaud
Foundation. The selected student will have access to the
facilities of INESC-ID and the MLKD group
(https://mlkd.idss.inesc-id.pt/), including computing
facilities that include four DELL PowerEdge C41402 servers,
eight NVIDIA 32GB Tesla V100S and eight NVIDIA 64GB Tesla
A100, among other computing servers
(https://mlkd.idss.inesc-id.pt/cluster) Representation learning of animal behaviorSupervised by Arlindo L. Oliveira and Adrien JouaryOver the past decade, several methods have been developed
that allow high-throughput automated quantification of animal
behavior. Advances in computer vision make it possible to
automatically track multiple body points. And continuous
movements can be decomposed into a sequence of meaningful
elementary units. In this project, we aim to build a latent
variable model of a large dataset of zebrafish larva behavior.
The behavior of each larva consists of a sequence of
stereotypical tail movements. The model will be trained to
perform prediction of future action. Once the model is trained
we will explore transfer learning by using the representation
from the model to detect the effect of drug treatment. For
this, we will use a dataset of the larva behavior in response
to 10 pharmacological compounds at different concentrations.
Our goal is to learn the internal state of the animal using
this approach, which could be useful for studying the brain
and improving the detection of drug-induced behavioral
changes. Our approach holds promise for neuroscience and
preclinical research, as careful measurements of animal
behavior have proven to be an important complement to modern
techniques for recording and manipulating neural circuits.
Marques, J.C., Lackner, S., Félix, R. and Orger, M.B., 2018.
Structure of the zebrafish locomotor repertoire revealed with
unsupervised behavioral clustering. Current Biology, 28(2),
pp.181-195. // Wiltschko, A.B., Tsukahara, T., Zeine, A.,
Anyoha, R., Gillis, W.F., Markowitz, J.E., Peterson, R.E.,
Katon, J., Johnson, M.J. and Datta, S.R., 2020. Revealing the
structure of pharmacobehavioral space through motion
sequencing. Nature neuroscience, 23(11), pp.1433-1443. //
Oord, A.V.D., Li, Y. and Vinyals, O., 2018. Representation
learning with contrastive predictive coding. arXiv preprint
arXiv:1807.03748. Currently, there are no dissertations open for
application.