BBL WiMLDS

These sessions are an initiative by the WiMLDS Paris meetup group. Each 3rd Thursday of the month, during lunchtime, we take the time to discuss Machine Learning and Data Science papers that we have found interesting. It's also a good opportunity to meet ML-passionate people in Paris.

I've been in charge of organizing them for the year 2019/2020 and I had the chance to organize 8 paper reading sessions, as well as introducing a good number of papers to our members.

You can find below the list of papers that were discussed during the different sessions.

For each one of these BBLs, you will find

• The paper that was studied
• The slides
• Jupyter hackings if a demo was prepared

Sessions

• BBL #1 (2019-09-19):

  • Learning Embeddings for Product Size Recommendations (K Dogani, M Tomassetti, et al.)

• BBL #2 (2019-10-17):

  • Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (J Buolamwini, T Gebru)

• BBL #3 (2019-11-21):

  • Uncovering Bias in Ad Feedback Data Analyses & Applications (M Bron, M Lalmas, et al.)
  • Deep Unfolding of a Proximal Interior Point Method for Image Restoration (C. Bertocchi1, E. Chouzenoux, et al.) (introduced by Cécile Della Valle)

• BBL #4 (2020-02-20):

  • Inverting The Generator Of A Generative Adversarial Network (Antonia Creswell and Anil A Bharath)

• BBL #5 (2020-03-19):

  • An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis (X Shi, Q Dou, et al.) (introduced by Marie Sacksick)
  • CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant (Y Jernite, K Srinet, et al.)

• BBL #6 (2020-04-16):

  • Attention Is All You Need (A Vaswani, N Parmar, et al.) (introduced by Maroua Maachou)
  • Graph Convolution for Multimodal Information Extraction from Visually Rich Documents (X Liu, F Gao, et al.) (introduced by Chloé Laurent)

• BBL #7 (2020-05-26):

  • How can they know that? A study of factors affecting the creepiness of recommendations (Helma Torkamaan, Catalin-Mihai Barbu, Jürgen Ziegler)
  • SynSin: End-to-end View Synthesis from a Single Image (Olivia Wiles, Georgia Gkioxari et al) (introduced by Juliette Tisseyre)

• BBL #8 (2020-06-18):

  • Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning (Aman Agarwal, Mamta Mittal, et al)
  • Towards Robust Deep Neural Networks for Affect and Depression Recognition (Emna Rejaibi, Daoud Kadoch, et al) (introduced by Farah Dhouib)