Month: November 2019

AAII Seminar: November 20, 2019

Title: Hidden Physics Models: Machine Learning of Non-Linear Partial Differential Equations

Speaker: Maziar Raissi (Brown University)

Abstract: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics.

@UCSC: E2-215 at 12pm PT

Online:  https://ucsc.zoom.us/j/468337241

AAII Seminar: November 6, 2019 at 12pm

Title:  TRIMAP: LARGE-SCALE DIMENSIONALITY REDUCTION USING TRIPLETS
Speakers: Ehsan Amid and Manfred Warmuth (UCSC / visiting Google Brain)

Abstract:  We introduce “TriMap”; a dimensionality reduction technique based on triplet constraints that preserves the global accuracy of the data better than the other commonly used methods such as t-SNE, LargeVis, and UMAP. To quantify the global accuracy, we introduce a score which roughly reflects the relative placement of the clusters rather than the individual points. We empirically show the excellent performance of TriMap on a large variety of datasets in terms of the quality of the embedding as well as the runtime. On our performance benchmarks, TriMap easily scales to millions of points without depleting the memory and clearly outperforms t-SNE, LargeVis, and UMAP in terms of runtime.

@UCSC:  E2-215  at 12pm PT

Online: https://ucsc.zoom.us/j/120429746

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