Everyone is talking about GPT-3, so, we talked about GPT-3. This seems to be the paper that started it all, “Language Models are Few-Shot Learners.” The paper is long with 75 pages, but there are 31 authors, so there are ~2.42 pages per author. That should make it easy. Probably we will only have time …
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We began a new thread related to ethics in AI with papers focus on papers by Buolamwini and Gebru. AI is now used routinely to make decisions that once were made by people in areas ranging from hiring to policing to social matchmaking. It seems fair to scrutinize these applications for ethics and fairness. Particularly …
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Google Research recently open sourced TaPas, a system for doing natural language queries on tabular data. The model is fully differentiable and based on BERT. We read the paper, “TAPAS: Weakly Supervised Table Parsing via Pre-training.” We ran the code from google research repo on a virtual machine and saw both the power of the …
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For this first session of 2021, we did almost a pure tutorial session. We covered BERT again, but went through the experience using using TensorFlow 2.3 and Hub on a virtual machine. Along with the tutorial, we created a small repo with requirements and some instructions.
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.