Natural science will in time subsume2/28/2023 ![]() Our framework suggests two strategies for representing and sampling mixed random variables, an extrinsic (“sample-and-project”) and an intrinsic one (based on face stratification). From this measure, we introduce new entropy and Kullback-Leibler divergence functions that subsume the discrete and differential cases and have interpretations in terms of code optimality. We build rigorous theoretical foundations for these hybrids, via a new “direct sum” base measure defined on the face lattice of the probability simplex. In the second part, I will introduce mixed random variables, which are in-between the discrete and continuous worlds. ![]() ![]() Variants of these sparse transformations have been applied with success to machine translation, natural language inference, visual question answering, and other tasks. Entmax transformations are differentiable and (unlike softmax) they can return sparse probability distributions, useful to build interpretable attention mechanisms. The building block is a family of sparse transformations called alpha-entmax, a drop-in replacement for softmax, which contains sparsemax as a particular case. In the first part of the talk, I will describe how sparse modeling techniques can be extended and adapted for facilitating sparse communication in neural models. Reconciling these two forms of communication is desirable for generating human-readable interpretations or learning discrete latent variable models, while maintaining end-to-end differentiability. Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols. André Martins (LST Lisbon) gives a presentation on “From Sparse Modeling to Sparse Communication” ![]() André Martins (LST Lisbon) will give a presentation on “From Sparse Modeling to Sparse Communication.”ġ5.00: Opening by Dr. In this series of four webinars, the lab will focus on causality, information retrieval, natural language processing, and reinforcement learning. The Mercury Machine Learning Lab (MMLL) would like to invite you to the MMLL online seminar series. ![]()
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