/MLR

ML Research Resources

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Machine Learning Research

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1. Project Topic

1.1. Existing research

1.2. Datasets and Tasks

1.13. Tools

  • Overleaf for writing AI research papers with LaTeX.
  • Mendeley for reference BibTeX.

2. Project Advice

Processing Data

  • StanfordNLP, a Python library providing tokenization, tagging, parsing, and other capabilities.

  • Software from the Stanford NLP Group

  • NLTK, a lightweight Natural Language Toolkit package in Python.

  • spaCy, another Python package that can do preprocessing, but also includes neural models (e.g. Language Models)

3. Top Tiers ML&AI Conferences

  • Machine Learning

    • NeurIPS
      • Neural Information Processing Systems (formerly abbreviated NIPS). NeurIPS has gotten huge over the past few years as AI has become so important. Has a focus on neural networks, but not exclusively.
    • ICML
      • International Conference on Machine Learning. Has a general machine learning focus.
    • ICLR
      • International Conference on Learning Representations. ICLR was really the first conference focused on deep learning. It’s called “learning representations” because the motivation behind deep learning is to automatically learn higher-level features, or representations, that summarize data in useful ways. Deep Learning describes the structure of our current best solution to the problem of learning these representations.
    • AISTATS
  • Computer Vision

    • CVPR
      • Computer Vision and Pattern Recognition.
    • ICCV
      • International Conference on Computer Vision.
    • ECCV
  • Natural Language Processing

  • Data

  • Artificial Intelligence

    • AAAI
      • Association for the Advancement of Artificial Intelligence. AAAI is a little more applications focused, and a little less theoretical than some of the other AI conferences.
    • ICANN
    • IJCAI
    • UAI

4. Courses