- The Rise of Vector Data - May 25, 2021.
Embedding models convert raw data such as text, images, audio, logs, and videos into vector embeddings (“vectors”) to be used for predictions, comparisons, and other cognitive-like functions.
- Graph Representation Learning: The Free eBook - Jan 19, 2021.
This free eBook can show you what you need to know to leverage graph representation in data science, machine learning, and neural network models.
- Disentangling disentanglement: Ideas from NeurIPS 2019 - Jan 15, 2020.
This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next.
- Text Encoding: A Review - Nov 22, 2019.
We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.
- The State of Transfer Learning in NLP - Sep 13, 2019.
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and Sebastian Ruder. This post highlights key insights and takeaways and provides updates based on recent work.
- Most impactful AI trends of 2018: The rise of ML Engineering - Mar 1, 2019.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
- A “Weird” Introduction to Deep Learning - Mar 30, 2018.
There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.
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- UNIGE: PhD position on Machine Learning for representation learning - Jun 23, 2014.
Develop new machine learning methods for representation learning. Position is funded for three years. Applications submitted by June 30 will be given priority.