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ML Student Seminar on Dec 2 2021: Adversarial Learning on Time Series Forecasting

by Katharina Dost | Nov 25, 2021 | ML Student Seminar, News

Where and when: Thursday, Dec 2 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Mark Chen, supervised by Joerg Wicker and Gill Dobbie Abstract: Time series forecasting is one of the main topics which involves time series data. It predicts...

AI Reading Group on Nov 25 2021: The neural architecture of language: Integrative modeling converges on predictive processing

by Katharina Dost | Nov 22, 2021 | AI Reading Group, News

Where and when: Thursday, Nov 25 at 2-3pm in Google Meet (see the calendar invite for the link). Abstract The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are...

ML Student Seminar on Nov 18 2021: Beyond Recall: Teaching High Quality Language Models to Generalise to Unseen Compositional Questions

by Katharina Dost | Nov 15, 2021 | ML Student Seminar, News

Where and when: Thursday, Nov 18 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Tim Hartill, supervised by Michael Witbrock and Pat Riddle Abstract: Sequence-to-sequence Transformer-based Language Models pretrained on large text corpora...
ML Student Seminar on Nov 4 2021: New Zealand Bat Audio Analysis

ML Student Seminar on Nov 4 2021: New Zealand Bat Audio Analysis

by Katharina Dost | Oct 28, 2021 | ML Student Seminar, News

Where and when: Thursday, Nov 4 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Tsz Fung Ip (Roy), Master of Data Science student Abstract: Due to urbanization in the past few decades, various species of bats in New Zealand are facing...

AI Reading Group on Oct 28 2021: The Computational Gauntlet of Human-Like Learning

by Katharina Dost | Oct 21, 2021 | AI Reading Group, News

Where and when: Thursday, Oct 28 at 2-3pm in 303S-561 or Google Meet (see the calendar invite for the link). Abstract In this paper, we pose a challenge for AI researchers: to develop systems that learn in a human-like manner. We briefly review the history of machine...
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