We develop a deterministic single-pass algorithm for latent Dirichlet allocation (LDA) in order to process received documents one at a time and then discard them in an excess text stream. Our algorithm does not need to store old statistics for all data. The proposed algorithm is much faster than a batch algorithm and is comparable to the batch algorithm in terms of perplexity in experiments. Papers published at the Neural Information Processing Systems Conference.
So, when I need artificial intelligence to automate my business, why can't I get recommendations for which machine learning algorithm best suits my individual needs? Every business is unique, and there are hundreds of algorithms available, each one with individual strengths and weaknesses. Just like I don't look at every individual book when choosing which one to read, I don't have the time, resources, or knowledge to try out each and every algorithm. I want artificial intelligence to recommend a short list of algorithms for me to try on my data.
With the increase in available data parallel machine learning has become an increasingly pressing problem. In this paper we present the first parallel stochastic gradient descent algorithm including a detailed analysis and experimental evidence. Unlike prior work on parallel optimization algorithms our variant comes with parallel acceleration guarantees and it poses no overly tight latency constraints, which might only be available in the multicore setting. Our analysis introduces a novel proof technique --- contractive mappings to quantify the speed of convergence of parameter distributions to their asymptotic limits. As a side effect this answers the question of how quickly stochastic gradient descent algorithms reach the asymptotically normal regime.
PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients.
Pain-predicting AI could help doctors discover if any of their patients are faking it. Isaac Asimov's First Law of Robotics states that a robot may not injure a human being or, through inaction, allow a human being to come to harm. But that does not mean a computer can't tell us whether a person is in pain -- and then neatly rank that pain level into some objective measure, like a computer science textbook written by the author of Fifty Shades of Grey. The work in question was carried out by researchers at the Massachusetts Institute of Technology (MIT). They developed an artificial intelligence that is able to predict how much pain a person is in by looking at an image.