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Creators/Authors contains: "Alemi, Alexander A."

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  1. ; ; ; ; (, Advances in neural information processing systems)
    Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks. We show that while knowledge distillation can improve student generalization, it does not typically work as it is commonly understood: there often remains a surprisingly large discrepancy between the predictive distributions of the teacher and the student, even in cases when the student has the capacity to perfectly match the teacher. We identify difficulties in optimization as a key reason for why the student is unable to match the teacher. We also show how the details of the dataset used for distillation play a role in how closely the student matches the teacher --- and that more closely matching the teacher paradoxically does not always lead to better student generalization. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, On the use of the arXiv as a dataset)
    The arXiv has collected 1.5 million pre-print articles over 28 years, hosting literature from scientific fields including Physics, Mathematics, and Computer Science. Each pre-print features text, figures, authors, citations, categories, and other metadata. These rich, multi-modal features, combined with the natural graph structure—created by citation, affiliation, and co-authorship—makes the arXiv an exciting candidate for benchmarking next-generation models. Here we take the first necessary steps toward this goal, by providing a pipeline which standardizes and simplifies access to the arXiv’s publicly available data. We use this pipeline to extract and analyze a 6.7 million edge citation graph, with an 11 billion word cor- pus of full-text research articles. We present some baseline classification results, and motivate application of more exciting generative graph models. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, Quantitative Finance)
    Full Text Available