Hello World and welcome. This is my first message here on this platform, in this forum, and I do mean this to be an invitation. I do not know who will read, but thank you for reading, especially if you are someone that I do not know. What I intend to create here in the …
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We returned to a topic for which we had previously had many sessions, that of NLP and word embeddings. We discussed “Deep contextualized word representations” by Peters et alia. We discussed the historical context of ELMo being popular as a bidirectional language model having the advantage over Word2vec of taking word order into account. For …
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We looked at a classical quantum-inspired algorithm by Erwin Tang in “A quantum-inspired classical algorithm for recommendation systems” and an associated follow up paper by Arrazola et alia “Quantum-inspired algorithms in practice.” This algorithm uses clever sampling techniques to approximate solutions for linear equations of the form Ax = b where x is unknown, essentially …
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We had taken a break from regular meetings, and when we returned, we switched gears to topics in quantum computing applied to machine learning. We discussed a few sections from “Machine learning & artificial intelligence in the quantum domain” by Vedran Dunjko and Hans J. Briegel.
We discussed two classic papers by Alex Graves. The oldest of these was “Generating Sequences With Recurrent Neural Networks” in which we especially focused on those parts about generating handwriting. The newer one, “DRAW: A Recurrent Neural Network For Image Generation” had several coauthors from Google DeepMind and explained how to use neural Turing machines …
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We wrapped up our discussion of clustering on graphs, still with a couple more sections from “Clustering and Community Detection in Directed Networks: A Survey” by Fragkiskos D. Malliarosa, Michalis Vazirgiannis. For the second half of the session, we switched gears and discussed a comparison between polynomial regression and neural networks that seemed both theoretically …
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We continued with our study of clustering methods applied to graph theory with “Clustering and Community Detection in Directed Networks: A Survey” by Fragkiskos D. Malliarosa, Michalis Vazirgiannis which describes methods for clustering graphs without discarding directional information.
Here we began a study of clustering methods applied to graph theory with the goal of understanding community detection in social networks. We began with an application from Oggier, Phetsouvanh and Datta, “BiVA: Bitcoin Network Visualization & Analysis” which applies clustering techniques and entropy centrality to produce visualizations of the transaction network defined by the …
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We continued studying graphs with social networking applications in mind. We read a paper with obvious importance to social networks, “Opinion maximization in social networks” by Aristides Gionis, Evimaria Terzi and Panayiotis Tsaparas. This paper described a game theoretical method for choosing members of a social network who can spread a change of opinion most …
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We continued studying graphs with social networking applications in mind. We read “On efficient use of entropy centrality for social network analysis and community detection” by Alexander G. Nikolaev, Raihan Razib, Ashwin Kucheriya which described a measurement of centrality. Think of a social network as network of merchants trading apples with their neighbors. Consider what …
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