[R] Beyond Vector Spaces: Compact Data Representations Differentiable Weighted Graphs

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Paper: https://arxiv.org/abs/1910.03524 (NeurIPS 2019) Code: https://github.com/stanis-morozov/prodige The paper proposes an embedding layer based on weighted graph instead of vectors. Intuitively, this layer learns to represent concepts/words by their relation to other. Trains by backprop w.r.t. graph edges. (Left) PRODIGE learned on a subset of MNIST. (Right) zoom-in of some clusters. […]

A Gentle Introduction to Bayesian Belief Networks

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Probabilistic models can define relationships between variables and be used to calculate probabilities. For example, fully conditional models may require an enormous amount of data to cover all possible cases, and probabilities may be intractable to calculate in practice. Simplifying assumptions such as the conditional independence of all random variables […]

How to Implement Bayesian Optimization from Scratch in Python

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Discover a Gentle Introduction to Bayesian Optimization. Global optimization is a challenging problem of finding an input that results in the minimum or maximum cost of a given objective function. Typically, the form of the objective function is complex and intractable to analyze and is often non-convex, nonlinear, high dimension, […]

[D] double blind review policy of AAAI/ICLR?

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Recently I noticed that some people promote their arxiv papers on their personal websites or social media right after the conference deadline, which violates the double blind review policy IMO. Is it a fancy way to cheat in the double-blind review process? Examples: ​ ​ The pinned tweet ​ The […]

[N] Udacity had an interventional meeting with Siraj Raval on content theft for his AI course

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​ According to Udacity insiders Mat Leonard @MatDrinksTea and Michael Wales @walesmd: ​ https://i.redd.it/yr5yg453tjo31.png Siraj has a habit of stealing content and other people’s work. That he is allegedly scamming these students does not surprise me one bit. I hope people in the ML community stop working with him. https://t.co/oiZmhWPWiN […]