This pilot project collects problems and metrics/datasets from the AI research literature, and tracks progress on them. You can use this notebook to see how things are progressing in specific subfields or AI/ML as a whole, as a place to report new results you've obtained, as a place to look for problems that might benefit from having new datasets/metrics designed for them, or as a source to build on for data science projects. At EFF, we're ultimately most interested in how this data can influence our understanding of the likely implications of AI. To begin with, we're focused on gathering it.
Jeremy is talking about that CNN maybe will take over by the end of the year. What would be the best solution for a time series with parallel parameters that normally use LSTM/GRU to solve before? For example predicting…
Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effectively.
Graph neural networks exploit relational inductive biases for data that come in the form of a graph. However, in many cases the graph is not available. Can we still use them?
ELI5 is a Python library which allows to visualize and debug various Machine Learning models using unified API. It has built-in support for several ML frameworks and provides a way to explain black-box models.
C. Scholz, J. Illig, M. Atzmueller, and G. Stumme. Proceedings of the 25th ACM Conference on Hypertext and Social Media, page 279--284. Santiago, Chile, ACM, (September 2014)