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1 . VAEs and Diffusion Models I. Discuss the difference between them and their potential uses in target applications. II . Why is one better

1. VAEs and Diffusion Models
I. Discuss the difference between them and their
potential uses in target applications.
II. Why is one better than the other for a given
application.
3. What is the purpose of Positional encoding in the context of a
transformer?
4. Describe a Multi-modal Transformer used within the context of
medical diagnosis or Emotion Recognition?
5. Consider a dataset of brain image segments (e.g., CAT scans);
discuss how a combination of generative models can be used for anomaly
detection.
6. Consider mapping Brain signals to its corresponding image that
is visualised;
I. Discuss how Diffusion models can be used in such a
use-case.
II. Relate to the challenges and how they can be overcome
using advanced composition of deep learning architecture.
3. Describe the difference between Supervised, Unsupervised and
Self Supervised learning in the context of a deep learning model such as a
VAE
4. Describe a use-case along with the dataset for a Graph NN
5. Could you explain the concept of data fusion and the
fundamental necessity for implementing a cross-attention mechanism
6. Discuss the ways in which Large Language Models (LLMs) might
augment or enhance your model

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