Using graph embeddings to explore deep neural network architectures

Convolutional 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. Many different architectures exist, that exhibit significant variations in performance, complexity and training cost. Using the appropriate transformations, it is possible to generate graph (or hypergraph) representations of deep neural network architectures, and these representations can be embedded into appropriate spaces that may be more amenable to performance quantification. This dissertation will explore the idea that graph embeddings of deep neural network architectures (and, possibly, weights) can be used to explore the architecture space in more effective ways than is possible today. Requisites: The student should have significant programming experience, and practical knowledge of machine learning languages and environments, such as PyTorch or TensorFlow. Notes: The work will be be developed in cooperation with research groups from the University of Tokyo and the Hong Kong Polytechnic, which have significant expertise in the graph embedding techniques that will be used in this work. 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.