Featured Event

  • The efficiency of kernel methods on structured datasets

    Wed, May 5, 2021, 4:30 pm
    Song Mei
    Inspired by the proposal of tangent kernels of neural networks (NNs), a recent research line aims to design kernels with a better generalization performance on standard datasets. Indeed, a few recent works showed that certain kernel machines perform as well as NNs on certain datasets, despite their separations in specific cases implied by theoretical results. Furthermore, it was shown that the induced kernels of convolutional neural networks perform much better than any former handcrafted kernels. These empirical results pose a theoretical challenge to understanding the performance gaps in kernel machines and NNs in different scenarios.

  • Barriers to Deploying Deep Learning Models During the COVID-19 Pandemic

    Wed, Apr 28, 2021, 12:00 pm
    Derek Driggs
    A promising application for deep learning models is in assisting clinicians with interpreting X-ray and CT scans, especially when treating respiratory diseases. At the onset of the COVID-19 pandemic, radiologists had to quickly learn how to identify a new disease on chest X-rays and CT scans, and use this information to decide how to allocate scarce resources like ventilators. Researchers around the world developed deep learning models to help clinicians with these decisions, and some models were deployed after only three weeks of testing.

  • CSML/Princeton Data Science Grant Presentation

    Fri, Apr 30, 2021, 4:00 pm
    Princeton Data Science Club Logo
    This event is meant to highlight independent projects that Princeton Data Science (PDS) Data Science Grant recipients have been working on throughout this past semester. The grant recipients will each be giving a short presentation detailing their projects, which range from creating a dynamic digital 3D model of Streicker Bridge on Princeton’s campus to detecting three different types of retinal diseases from medical scans using neural networks.

  • Machine Learning and Dynamical Systems meet in Reproducing Kernel Hilbert Spaces

    Wed, Apr 21, 2021, 12:00 pm
    Boumediene Hamzi
    Since its inception in the 19th century through the efforts of Poincaré and Lyapunov, the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models. From this perspective, the modeling of dynamical processes in applications requires a detailed understanding of the processes to be analyzed. This deep understanding leads to a model, which is an approximation of the observed reality and is often expressed by a system of Ordinary/Partial, Underdetermined (Control),

  • Princeton Research Day 2021

    Thu, May 6, 2021 (All day)
    Two women in masks digging up soil and plants as part of research.
    Princeton’s celebration of early-career research and creative work is back in an all-online format.

  • The One World Seminar on the Mathematics of Machine Learning

    Wed, Apr 7, 2021, 12:00 pm
    Bubacarr Bah
    In this talk we study the problem of signal recovery for group models. More precisely for a given set of groups, each containing a small subset of indices, and for given linear sketches of the true signal vector which is known to be group-sparse in the sense that its support is contained in the union of a small number of these groups, we study algorithms which successfully recover the true signal just by the knowledge of its linear  sketches. We derive model projection complexity results and algorithms for more general  group models than the state-of-the-art. We consider two versions of the classical Iterative Hard Thresholding algorithm (IHT). The  classical version iteratively calculates the exact projection of a vector onto the group model, while the approximate version (AM-IHT) uses a head- and a tail-approximation iteratively. We apply both variants to group models and analyze the two cases where the sensing matrix is a Gaussian matrix and a model expander matrix.

  • DataX Workshop: Social biases in machine learning and in human nature: What social scientists and computer scientists can learn from each other

    Fri, Apr 9, 2021, 8:00 am
    DataX Logo - A series of lines converging at a 45 degree angle to a point with DataX in the foreground

    Princeton DataX Workshop: Social Biases in Machine Learning and in Human Nature: What Social Scientists and Computer Scientists Can Learn From Each Other:   

    2-day virtual workshop explores social biases in machine learning and brings together cutting-edge innovative sociology, social psychology, cognitive science, and computer science perspectives on the interplay between stereotyping and human and artificial intelligence. 

    Host: Xuechunzi Bai & Susan T. Fiske

  • Leveraging Dataset Symmetries in Neural Network Prediction

    Mon, Mar 22, 2021, 12:30 pm
    Christine Allen-Blanchette

    Scientists and engineers are increasingly applying deep neural networks (DNNs) to modelling and design of complex systems. While the flexibility of DNNs makes them an attractive tool, it also makes their solutions difficult to interpret and their predictive capability difficult to quantify. In contrast, scientific models directly expose the equations governing a process but their applicability is restricted in the presence of unknown effects or when the data are high-dimensional.

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