The Limits of NLP
Data Skeptic24 Joulu 2019

The Limits of NLP

We are joined by Colin Raffel to discuss the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer".

Jaksot(589)

N-Beats

N-Beats

Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. Works Mentioned: N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio https://arxiv.org/abs/1905.10437 Social Media Linkedin Twitter

12 Heinä 202134min

Translation Automation

Translation Automation

Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future.

6 Heinä 202136min

Time Series at the Beach

Time Series at the Beach

Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work "Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation."

28 Kesä 202123min

Automatic Identification of Outlier Galaxy Images

Automatic Identification of Outlier Galaxy Images

Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior

21 Kesä 202136min

Do We Need Deep Learning in Time Series

Do We Need Deep Learning in Time Series

Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work "Do We Really Need Deep Learning Models for Time Series Forecasting?"

16 Kesä 202129min

Detecting Drift

Detecting Drift

Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time. Check out Sam's IBM statistics/ML blog at: http://www.research.ibm.com/haifa/dept/vst/ML-QA.shtml

11 Kesä 202127min

Darts Library for Time Series

Darts Library for Time Series

Julien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts.

31 Touko 202125min

Forecasting Principles and Practice

Forecasting Principles and Practice

Welcome to Timeseries! Today's episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices.

24 Touko 202131min

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