Reasoning for Complex Data through Self-Supervised Learning

Name: Reasoning for Complex Data through Self-Supervised Learning
Start: 2026-05-27T23:00:00.000Z
End: 2026-05-28T01:00:00.000Z
Location: Madison Public Library - Central

Reasoning for Complex Data through Self-Supervised Learning at Madison Public Library - Central in Madison, WI on Wednesday, May 27, 2026.

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When is Reasoning for Complex Data through Self-Supervised Learning?: Reasoning for Complex Data through Self-Supervised Learning takes place on Wednesday, May 27, 2026.
Where is Reasoning for Complex Data through Self-Supervised Learning taking place?: Reasoning for Complex Data through Self-Supervised Learning is at Madison Public Library - Central · Madison.
How do I attend Reasoning for Complex Data through Self-Supervised Learning?: Visit the venue website for details about Reasoning for Complex Data through Self-Supervised Learning.

Reasoning for Complex Data through Self-Supervised Learning

Reasoning for Complex Data through Self-Supervised Learning at Madison Public Library - Central in Madison, WI on Wednesday, May 27, 2026.

Conference

Never miss a show

Get a free daily email with your area shows and low-ticket alerts.

Frequently Asked Questions

When is Reasoning for Complex Data through Self-Supervised Learning?: Reasoning for Complex Data through Self-Supervised Learning takes place on Wednesday, May 27, 2026.
Where is Reasoning for Complex Data through Self-Supervised Learning taking place?: Reasoning for Complex Data through Self-Supervised Learning is at Madison Public Library - Central · Madison.
How do I attend Reasoning for Complex Data through Self-Supervised Learning?: Visit the venue website for details about Reasoning for Complex Data through Self-Supervised Learning.

*** Self-supervised learning deals with problems that have little or no available labeled data. Recent work has shown impressive results when underlying classes have significant semantic differences. We will discuss strategies to tackle to enable learning from unlabeled data even when samples from different classes are not prominently diverse. We approach the problem by leveraging novel ensemble-based clustering strategies where clusters derived from different configurations are combined to generate a better grouping for the data samples in a fully-unsupervised way. We will see results for Person Re-Identification and Text Authorship Verification but the techniques are useful in other applications as well. Moreover, we also detail recent efforts on Causal Analysis to refine AI methods. Anderson Rocha (F) is a Full Professor of Artificial Intelligence and Digital Forensics at the Institute of Computing, University of Campinas (Unicamp), Brazil. He is the head of the Artificial Intelligence Lab., Recod.ai, at Unicamp and was the former Director of the Institute for the 2019-2023 term. He holds a bachelor's degree in Computer Science (2003) from Federal University of Lavras, Brazil, a Masters Degree in Computer Science (2006) from Unicamp and a Ph.D. degree in Computer Science (2009) also from Unicamp, Brazil. Prof. Rocha is an elected affiliate of the Brazilian Academy of Sciences (ABC) and the Brazilian Academy of Forensic Sciences (ABC). He is a three-term elected member of the IEEE Information Forensics and Security Technical Committee (IFS-TC, 2011-2013, 2014-2016, 2023-2026) and a two-term former Chair of such committee (2015-2016, 2025-2026). He is a Microsoft Research (2011) and a Google (2017-2022) Research Faculty Fellow, an IEEE Fellow (2023), and IEEE Biometrics Distinguished Lecturer (2025-2027). In addition, in 2016, he was awarded the Tan Chin Tuan (TCT) Fellowship, and the Asia Pacific Artificial Intelligence Association Fellowship. Prof. Rocha has…