CS 519, Applied Machine Learning, Spring 2021, Paper Review

Requirements

Your Paper Review (min. 4 pages, max. 6 pages, 12 pt font, single-spaced, figs/tables at most 0.75 pages), due on Monday June 7 (submit a single PDF), must cover at least: Use 12pt font, single space. Use LaTeX if possible.

Hint: Google Scholar is your friend. Use it to find freely-available PDFs for journal papers, citation counts and trends, bibtex files, etc.

List of Candidate Papers (you must choose a paper from this list)

    bias, bias amplification, and fairness in machine learning (recommended)

  1. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints (2017)
  2. Feature-Wise Bias Amplification (2018)
  3. Equality of Opportunity in Supervised Learning (2016)

    large-scale machine learning

  4. XGBoost

    question answering and truly-blind competitions (recommended)

  5. SQuAD 1.0 (2016)
  6. SQuAD 2.0 (2018)
  7. CoQA (2018)

    deep reinforcement learning

  8. Atari: journal version (non-technical); conference version (more technical) (2015)
  9. AlphaGo (2016)
  10. AlphaGoZero (2017)
  11. AlphaZero (2018--2019)

    deep learning in language and vision

  12. show and tell: Show and Tell: A Neural Image Caption Generator, 2014--2015
  13. show, attend, and tell: Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, 2015--2016
  14. neural machine translation: Neural Machine Translation by Jointly Learning to Align and Translate 2014--2015
  15. sentence representation: Dependency-based Convolutional Neural Networks for Sentence Embeddings, 2015
  16. listen and tell: Audio Caption: Listen and Tell, 2019

    deep learning in biology

  17. protein folding: Distance-based protein folding powered by deep learning, 2019
  18. protein folding: AlphaFold: Improved protein structure prediction using potentials from deep learning, 2020
  19. RNA folding: RNA Secondary Structure Prediction By Learning Unrolled Algorithms, 2020
  20. RNA folding: RNA secondary structure prediction using deep learning with thermodynamic integration, 2021