Upcoming Events

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Events Archive

Beyond Supervised Learning for Biomedical Imaging

Beyond Supervised Learning for Biomedical Imaging
Many biomedical imaging tasks, such as 3D reconstruction, denoising, detection, registration, and segmentation, are ill-posed inverse problems. In this talk, I will present a flexible machine learning-based framework that has allowed us to derive efficient solutions for a variety of such problems, without relying on heavy supervision. I will...
Location: B205 Engineering Quadrangle
Speaker(s):
Mert R. Sabuncu
Cornell University’s School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering
Tags: CSML Seminar Series

Prediction with Confidence – General Framework for Predictive Inference

Prediction with Confidence – General Framework for Predictive Inference
We propose a general framework for prediction in which a prediction is in the form of a distribution function, called ‘predictive distribution function’. This predictive distribution function is well suited for prescribing the notion of confidence under the frequentist interpretation and  providing meaningful answers for prediction-related...
Location: Corwin Hall, Room 127
Speaker(s):
Regina Y. Liu
Rutgers, Department of Statistics
Tags: CSML Seminar Series

AI Journey with Intel Workshop

AI Journey with Intel Workshop
Michael Zephyr,  AI Developer Evangelist of Intel, will offer a survey of the company's AI tools including Intel® Optimized Tensorflow* (CPU-only), Intel OpenVino™ Toolkit for computer vision, and Intel® AI libraries. The workshop is open to all members of the campus research community. Please see the attached poster for speaker bio and more...
Location: 399 Julis Romo Rabinowitz Building
Speaker(s):
Michael Zephyr
AI Developer Evangelist of Intel
AI Journey with Intel Workshop
Tags: Research Computing Workshop

TensorFlow and PyTorch User Group

TensorFlow and PyTorch User Group
JAX is a system for high-performance machine learning research. It offers the familiarity of Python+NumPy and the speed of hardware accelerators, and it enables the definition and the composition of function transformations useful for machine-learning programs. In particular, these transformations include automatic differentiation, automatic...
Location: 120 Lewis Science Library
Speaker(s):
Peter Hawkins
Google AI Princeton
JAX: Accelerated machine-learning research via composable function transformations in Python
Tags: Research Computing Workshop

DataX Research Funding Information Session

DataX Research Funding Information Session

Thinking of applying for DataX research funding but would like more information? Join us for an information session!

The session aims to:

DataX Research Funding Information Session

DataX Research Funding Information Session

Thinking of applying for DataX research funding but would like more information? Join us for an information session!

The session aims to:

CSML 2019 Poster Session

CSML 2019 Poster Session
Location: Carl A. Fields Center, Multipurpose Room 104
Tags: Undergrad Certificate

Multiscale Model Reduction in Physics with Deep Networks

Multiscale Model Reduction in Physics with Deep Networks
Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning and physics. This talk shows deep convolutional neural network architectures take advantage of scale separation, symmetries and sparse representations. We introduce simplified architectures which can be analyzed mathematically. Scale...
Location: Peyton Hall Auditorium, Room 145
Speaker(s):
Stéphane Mallat
Collège de France, Flatiron Institute
Tags: Seminars

Princeton Research Day 2019

Princeton Research Day 2019

Princeton Research Day 2019 celebrates research and art from the natural sciences, social sciences, engineering, the arts and humanities. This year’s event will include something new – a full day of arts performances and lectures, in the third-floor Frist auditorium. The day will finish on the Mainstage with the...

Location: Frist Campus Center

The Power of Self-Learning Systems

The Power of Self-Learning Systems

Demis Hassabis will discuss the capabilities and power of self-learning systems. He will illustrate this with reference to some of DeepMind's recent breakthroughs, and talk about the implications of cutting-edge AI research for scientific and philosophical discovery.

Location: Wolfensohn Hall, Institute for Advanced Studies

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