ObjectNet
Andrei Barbu joins us to discuss ObjectNet - a new kind of vision dataset. In contrast to ImageNet, ObjectNet seeks to provide images that are more representative of the types of images an autonomous ...
7 Helmi 202038min
Visualization and Interpretability
Enrico Bertini joins us to discuss how data visualization can be used to help make machine learning more interpretable and explainable. Find out more about Enrico at http://enrico.bertini.io/. More fr...
31 Tammi 202035min
Interpretable One Shot Learning
We welcome Su Wang back to Data Skeptic to discuss the paper Distributional modeling on a diet: One-shot word learning from text only.
26 Tammi 202030min
Fooling Computer Vision
Wiebe van Ranst joins us to talk about a project in which specially designed printed images can fool a computer vision system, preventing it from identifying a person. Their attack targets the popula...
22 Tammi 202025min
Algorithmic Fairness
This episode includes an interview with Aaron Roth author of The Ethical Algorithm.
14 Tammi 202042min
Interpretability
Interpretability Machine learning has shown a rapid expansion into every sector and industry. With increasing reliance on models and increasing stakes for the decisions of models, questions of how mod...
7 Tammi 202032min
NLP in 2019
A year in recap.
31 Joulu 201938min
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".
24 Joulu 201929min
