| CARVIEW |
Anomaly Detection for Scientific Discovery Seminars (Season I)
Bring machine learning experts and domain scientists together
Introduction
Automatically detecting anomalies and distribution shifts is an essential aspect of modern science. For example, anomaly detection is used to search for new fundamental particles and forces of nature, anomalous galactic activities, novel molecular dynamics, and new medical conditions. Concurrently, the machine learning community is also interested in anomaly/out-of-distribution detection problems. The effectiveness and robustness of computational pipelines and autonomous systems must be ensured by inspecting and validating incoming data. Unfortunately, machine learning research in these areas is often disconnected from real-world applications, and limited to the common MNIST and CIFAR benchmarks. This initiative aims to bridge this gap, facilitating conversation between machine learning researchers and domain scientists. The goal is to forge collaborations by introducing the former to impactful applications and exposing the latter to useful methodologies and computational tools. The practical side of in-the-wild anomaly detection will also be discussed, with topics including model design, data pipelines, model validation, experimental process, and common pitfalls.
Speakers
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Thomas G. Dietterich
(Oregon State University)Machine Learning
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Jie Ren
(Google Brain)Machine Learning, Bioinformatics
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Sharon Y. Li
(University of Wisconsin-Madison)Machine Learning
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Danilo Bzdok
(McGill University)Neuroscience
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Kiri L. Wagstaff
(NASA Jet Propulsion Laboratory)Astronomy/Planetary Science
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V. Ashley Villar
(Pennsylvania State University)Astronomy
Schedule
(Hover over the talk titles to see the abstracts)
| Dec. 02, 2021, 1:00 pm EST | Jie Ren |
Exploring the limits of out-of-distribution detection in vision and biomedical applications |
[Recording][Slides] |
| Jan. 13, 2022, 1:00 pm EST | Thomas G. Dietterich |
Anomaly Detection in Shallow and Deep Learning |
[Recording][Slides] |
| Feb. 10, 2022, 1:00 pm EST | Sharon Yixuan Li |
Challenges and Opportunities in Out-of-distribution Detection |
[Recording][Slides] |
| Mar. 17, 2022, 1:00 pm EDT | V. Ashley Villar |
Anomaly Detection for Cosmic Explosion |
[Recording][Slides] |
| TBD 2022 | Kiri L. Wagstaff | [Title] | [Recording] |
| TBD 2022 | Danilo Bzdok | [Title] | [Recording] |
Interactives
[Topical Discussion Sessions, TBA]
Organizers
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Taoli Cheng (Mila, University of Montreal)
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Eric Nalisnick (University of Amsterdam)
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Joseph Paul Cohen (AIMI, Stanford University)
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Benjamin Nachman (LBNL)
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Dan Hendrycks (UC Berkeley)
Code of Conduct
We are committed to create an open and friendly environment for scientific sharing and communication. All participants (including organizers, speakers, and attendees) are required to comply with the Code of Conduct from the NeurIPS community. Any violations could be communicated to the organizers at ad4sd2021@gmail.com.