Upcoming Events

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Events Archive

Breaking the Sample Size Barrier in Statistical Inference and Reinforcement Learning

A proliferation of emerging data science applications require efficient extraction of information from complex data. The unprecedented scale of relevant features, however, often overwhelms the volume of available samples, which dramatically complicates statistical inference and decision making.

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HEE Seminar- Taylor Faucett-UCI-Physics Learning from Machines Learning

Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without...

Location: Zoom
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Using Code Ocean in the Sciences and Engineering: Bringing computational reproducibility to your research collaborations

Computational analyses are playing an increasingly central role in research. However, many researchers have not received training in best practices and tools for reproducibly managing and sharing their code and data. This is a step-by-step, practical workshop on managing your research code and data for computationally reproducible collaboration...

Conditional Sampling with Monotone GANs: Modifying Generative Models to Solve Inverse Problems

The One World Seminar Series on the Mathematics of Machine Learning is an online platform for research seminars, workshops and seasonal schools in theoretical machine learning.

Location: https://www.oneworldml.org/home
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Metamaterials Design and Manufacturing: Perspectives From Biology and Artificial Intelligence

After billions of years of evolution, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and...

Deep Learning: It’s Not All About Recognizing Cats and Dogs

In this seminar, we will examine the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalized and recommendation models consumes the highest number of compute cycles among all deep learning use cases. For AI inference, personalization and...
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Analysis of Stochastic Gradient Descent in Continuous Time

Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional. In this work, we introduce the stochastic gradient process as a continuous-time representation of stochastic gradient descent.

Location: https://www.oneworldml.org/home
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Tags: Seminars

Engage 2020

This multi-day, virtual conference will help create new connections among Princeton innovators and leaders in entrepreneurship, industry, nonprofit organizations, and government in the state, regional and global innovation ecosystems. Engage 2020 brings together a roster of accomplished academics, inventors, and entrepreneurs from science,...

Introduction to the Machine Learning Libraries

The Princeton HPC clusters offer several machine learning (ML) software libraries. Some are straightforward to use while others need to be installed and are highly configurable. Additional complications arise when job scheduler scripts need to be written to take advantage of multi-threading and/or GPUs.

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Intro to Data Analysis using Python

This workshop will get students started in data analysis using the pandas Python package. It will briefly cover different components of data analysis and connect them with the goal of extracting meaning from data. We will go over an example to illustrate the data analysis process from beginning to end.

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