Adrien Guille — Index - Research - Teaching - Publications - Code

Teaching

Representation Learning for NLP (M2 Data Mining)

Here is a list of suggested readings for this course:

Pre-Trained Word Representations

Main techniques to learn vector space representations of words

• SGNS: Distributed Representations of Words and Phrases and their Compositionality
• Glove: Global Vectors for Word Representation
• Link between SGNS, GloVe & PMI: Neural Word Embedding as Implicit Matrix Factorization

Supervised Learning with Pre-Trained Word Representations

Main neural architectures to solve document-level classification tasks

• CNN: Convolutional Neural Networks for Sentence Classification
• RNN & GRU: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Sequence to Sequence learning and attention mechanism

• Seq2Seq: Sequence to Sequence Learning with Neural Networks

• Dot-product Attention: Effective Approaches to Attention-based Neural Machine Translation

• Transformer: Attention is All You Need

  Supervised Learning with Pre-Trained Language Models

• BERT: Bidirectional Encoder Representations from Transformers

Data Mining (DUT2 Statistique et Informatique Décisionnelle)

Course material available on Moodle.

Bases de la programmation (DUT1 Statistique et Informatique Décisionnelle)

Course material available on Moodle.