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  • HDSI/TILOS Seminar | Rob Nowak | What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications

    Halıcıoğlu Data Science Institute (HDSI), Room 123, 3234 Matthews Ln, La Jolla, CA, 92093, United States

    When Wednesday July 24th 10:00am *Updated Where: HDSI 123 * Updated Zoom Info: https://ucsd.zoom.us/j/99334315002 *Updated Title: What Kinds of Functions do Neural Networks Learn?  Theory and Practical Applications Abstract:  This talk presents a theory characterizing the types of functions neural networks learn from data. Specifically, the function space generated by deep ReLU networks consists of compositions of functions from the […]

  • MathWorks Technical Seminar: AI & Machine Learning in Real-World Systems

    Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

    Beyond their use in traditional data analytics problems, AI and Machine Learning techniques are changing the way real-world complex systems (like vehicles, airplanes, and even industrial production lines) are designed, tested and fabricated. When building these systems, engineers rely heavily on modeling and computer-assisted simulations. This seminar showcases how MATLAB and Simulink can help integrate […]

  • HDSI Seminar – Generative Social Choice | Ariel Procaccia

    Seminar Series
    Halıcıoğlu Data Science Institute Room 123, 3234 Matthews Ln, La Jolla, CA 92093, USA

    Talk Information: When Monday Oct 21st 1:00pm Where: HDSI MPR 123 Zoom Info: http://bit.ly/HDSI-Seminars Title: Generative Social Choice Abstract: "The mathematical study of voting, social choice theory, has traditionally only been applicable to choices among a few predetermined alternatives, but not to open-ended decisions such as collectively selecting a textual statement. This limitation is addressed by generative social choice, a design methodology […]

  • Deep Learning: a Non-parametric Statistical Viewpoint

    Atkinson Hall, Fourth Floor

    ABSTRACT The advent of deep learning has completely revolutionized how we perceive data to obtain superhuman performance across all fields of modern science. However, despite the remarkable empirical successes of deep learners, the theoretical guarantees for their statistical accuracy remain rather pessimistic. In particular, the data distributions on which deep learners are generally applied, such […]

  • HDSI Seminar – Maksim Kitsak -Modeling and Inference of Complementarity Mechanisms in Networks.

    Seminar Series
    Halıcıoğlu Data Science Institute, 3234 Matthews Ln, La Jolla, CA 92093, USA Room 123

    Talk Information: When Wednesday Oct 30th 1:00pm Where: HDSI MPR 123 Zoom Info: http://bit.ly/HDSI-Seminars Title: Modeling and Inference of Complementarity Mechanisms in Networks. Abstract: "In many networks, including networks of protein-protein interactions, interdisciplinary collaboration networks, and semantic networks, connections are established between nodes with complementary rather than similar properties. What is complementarity? The Oxford Dictionary asserts that […]

  • Revisiting Scalarization in Multi-Task Learning | Prof. Han Zhao

    Seminar Series
    Halıcıoğlu Data Science Institute (HDSI), Room 123, 3234 Matthews Ln, La Jolla, CA, 92093, United States

    Title: Revisiting Scalarization in Multi-Task Learning Abstract: Linear scalarization, i.e., combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recent years, there has been a surge of interest in developing Specialized Multi-Task Optimizers (SMTOs) that treat MTL as a multi-objective optimization problem. […]

  • HDSI Seminar – Hongzhe Li

    Seminar Series
    Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

    When Wednesday Feb 5th 2:00pm Where: Computer Science & Engineering (CSE) 1st floor, Seminar Room 1242 Title: Fréchet Regression of Random Objects on Vector Covariates and Its applications for Single Cell RNA-seq Data Analysis Abstract: Population-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the […]

  • HDSI Seminar – Victor Minces – The Sound of Data

    Seminar Series
    Halıcıoğlu Data Science Institute Room 123, 3234 Matthews Ln, La Jolla, CA 92093, USA

    When Friday, February 21st Where: HDSI MPR 123 Title: The Sound of Data Speaker: Victor Minces Abstract: In this talk, Dr. Minces will give an overview of his career and how it led to the development of Listening to Waves, a program that creates playful activities and web applications that connect music with science through data visualization and sonification. He […]

  • Seminar: Deep Learning Theory in the Age of Generative AI – Sadhika Malladi

    CSE 1242

    Monday, March 17 11:00 AM - 12:00 PM (PST) CSE 1242  Title: Deep Learning Theory in the Age of Generative AI Abstract: Modern deep learning has achieved remarkable results, but the design of training methodologies largely relies on guess-and-check approaches. Thorough empirical studies of recent massive language models (LMs) is prohibitively expensive, underscoring the need for […]

  • Seminar – Jeremy Bernstein – Metrized Deep Learning

    Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

    Jeremy Bernstein MIT CSAIL Monday, March 31 11:00 AM - 12:00 PM (PST) CSE 1242 Title: Metrized Deep Learning Abstract: We build neural networks in a modular and programmatic way using software libraries like PyTorch and JAX. But optimization theory has not caught up to the flexibility of this paradigm, and practical advances in neural net […]