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MVA-2023-ALTEGRAD

Labs from the course ALTEGRAD (Advanced Learning for Text and Graph Data)
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Lab 1 : Neural Machine Translation

  • Sequence to Sequence (seq2seq) architectures.
  • Implementation of the Neural Machine Translation (NMT) model from This paper
  • Study of Recurrent Neural Networks
  • Study of Global Attention Mechanism

Lab 2 : Graph Mining

  • Dynamics of a Real-World Graph.
  • Community Detection / Clustering; spectral clustering, Modularity.
  • Graph Classification usign Graph Kernels.

Lab 3 : Transfer Learning in NLP

  • Generative pre-training of a language model.
  • Transformer Model.
  • Vocabulary and Tokenization.

Lab 4 : NLP Frameworks

  • Pretrain and finetuning of transformer based language models
  • Fairseq / LoRa (Low-Rank Adaptation)
  • ROBERTaSmall Model

Lab 5 : Deep Learning for Graphs 1/2

  • Node Embeddings; DeepWalk.
  • Graph Neural Networks; Implementation, Node Classification.

Lab 6 : Deep Learning for Graphs 2/2

  • Graph Neural Networks; expressive power of GNNs.

Lab 7 : Learning on Sets and Graph Generative Models

  • DeepSets
  • Graph Generation with Variational Graph Autoencoders

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