Consulta ofertas de otros estudios y especialidades
Boltzmann Machines are probabilistic models developed in 1985 by
D.H. Ackley, G.E. Hinton and T.J. Sejnowski. In 2006, Restricted
Boltzmann Machines (RBMs) were used in the pre-training step of
several successful deep learning models, leading to a new renaissance
of neural networks and artificial intelligence.
In spite of their nice mathematical formulation, there are a number of
issues that are hard to compute:
Therefore, in practice we have to approximate both the computation of
the probabilities and several components of the learning process
itself. These drawbacks have prevented RBMs to show their real
potential as truly probabilistic models.
Currently, we are working on trying to improve several of the unsolved
issues related to RBMs:
These works have opened new lines of research, some of which can be
the topic of a Master's Thesis. The scope and degree of depth of the
work can be adapted to the estimated times to complete the Thesis. For
further details, contact Enrique Romero (eromero@cs.upc.edu).
This project aims to analyze the prediction capability of Optical Coherence Tomography Angiography (OCTA) images for Diabetes Mellitus (DM) and Diabetic Retinopathy (DR,) in a large high-quality image dataset from previous research projects carried out in the field of Ophthalmology (Fundacio¿ La Marato¿ TV3, Fondo Investigaciones Sanitarias, FIS). OCTA is a newly developed, non-invasive, retinal imaging technique that permits adequate delineation of the perifoveal vascular network. It allows the detection of paramacular areas of capillary non perfusion and/or enlargement of the foveal avascular zone (FAZ), representing an excellent tool for assessment of DR.
A more detailed description of the project can be found in
https://www.cs.upc.edu/~eromero/Downloads/Retina-TFM-Project-01.pdf
The project is proposed in collaboration with Javier Zarranz Ventura
(Institut Clínic d'Oftalmologia, ICOF, Hospital Clínic de Barcelona, and
Institut d'Investigacions Biomèdiques August Pi I Sunyer, IDIBAPS),
which would provide a large annotated database to develop the project. For further information, please contact Alfredo Vellido (avellido@cs.
upc.edu) or Enrique Romero (eromero@cs.upc.edu).
Apply diffusion-based image generative models to convert videos into a cartoon style (e.g. from a sample image or a descriptive text). Depending on the obtained results a dictionary of styles may be created.
Recently, diffusion-based image generative models have obtained amazing results in multiple tasks such as text-conditioned image generation. They have been also successfully applied to image translation tasks, in which an input image is converted into a target domain defined by, e.g., the appearance of an example image (style transfer), text condition or in other ways. Image cartoonization is a particular case of image translation in which the goal is to apply to the input image a cartoon style. While full image or video cartoonization could be seen as a threat by animation professionals, the possibility to use cartoonized image or video backgrounds (without people) would be an innovation opportunity.
The student will have to implement different learning algorithms of Restricted Boltzmann Machine (RBM) neural networks using CUDA, and compare the performance against a standard CPU implementation.
Gradient descend based RBM learning is a computationally expensive procedure that requires the computation of a probability normalization constant (called the Partition function) that involves the computation of an exponentially large number of terms. In order to bypass that, different approximations exist, the most celebrated one being the Contrastive Divergence (CD) algorithm [1].
In this project we want to explore an alternative RBM model based on archetype (ARC) learning, described in [2]. The student will have to implement both the standard CD and the ARC algorithms in CUDA, and compare their performance in common benchmarking datasets.
[1] "Training restricted Boltzmann machines: An introduction",
Asja Fischer and Christian Igel, Pattern Recognition 47 (2014) 25-39
[2] "The emergence of a concept in shallow neural networks"
Elena Agliari, Francesco Alemanno, Adriano Barra, Giordano De Marzo, Neural Networks 148 (2022) 232-253
Study and development of a Reinforcement Learning system for the automatization of dwelling plan generation in the architecture domain
Development of an automatic plan generation alternative to
https://arxiv.org/abs/2004.13204v1
http://staff.ustc.edu.cn/~fuxm/projects/DeepLayout/index.html
with Reinforcement Learning
This project will be carried out in collaboration with GusanoFilms
(https://www.gusano.org) audiovisual producers. Its main goal is to
develop a tool, based on Machine Learning state-of-the-art methods,
for semi-automatic segmentation in multivariate temporal series of
images according to similarities among the elements in the image.
The project could be divided into different subtasks:
This project will be carried out in collaboration with GusanoFilms
(https://www.gusano.org) audiovisual producers. Its main goal is to
develop a tool, based on Machine Learning state-of-the-art methods,
for semi-automatic detection of relevant transitions in multivariate
temporal series of images.
The project could be divided into different subtasks:
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