[MINI] One Shot Learning
Data Skeptic22 Syys 2017

[MINI] One Shot Learning

One Shot Learning is the class of machine learning procedures that focuses learning something from a small number of examples. This is in contrast to "traditional" machine learning which typically requires a very large training set to build a reasonable model.

In this episode, Kyle presents a coded message to Linhda who is able to recognize that many of these new symbols created are likely to be the same symbol, despite having extremely few examples of each. Why can the human brain recognize a new symbol with relative ease while most machine learning algorithms require large training data? We discuss some of the reasons why and approaches to One Shot Learning.

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(601)

Sustainable Recommender Systems for Tourism

Sustainable Recommender Systems for Tourism

In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates...

9 Loka 202538min

Interpretable Real Estate Recommendations

Interpretable Real Estate Recommendations

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper "Z-REx: Human-Interpr...

22 Syys 202532min

Why Am I Seeing This?

Why Am I Seeing This?

In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri ...

8 Syys 202549min

Eco-aware GNN Recommenders

Eco-aware GNN Recommenders

In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks...

30 Elo 202544min

Networks and Recommender Systems

Networks and Recommender Systems

Kyle reveals the next season's topic will be "Recommender Systems".  Asaf shares insights on how network science contributes to the recommender system field.

17 Elo 202517min

Network of Past Guests Collaborations

Network of Past Guests Collaborations

Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.

21 Heinä 202534min

The Network Diversion Problem

The Network Diversion Problem

In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing...

6 Heinä 202546min

Complex Dynamic in Networks

Complex Dynamic in Networks

In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how inf...

28 Kesä 202556min

Suosittua kategoriassa Tiede

rss-poliisin-mieli
tiedekulma-podcast
rss-mita-tulisi-tietaa
docemilia
filocast-filosofian-perusteet
menologeja-tutkimusmatka-vaihdevuosiin
rss-duodecim-lehti
sotataidon-ytimessa
rss-tiedetta-vai-tarinaa
utelias-mieli
radio-antro
rss-bios-podcast
rss-ranskaa-raakana
rss-kasvatuspsykologiaa-kaikille
rss-luontopodi-samuel-glassar-tutkii-luonnon-ihmeita
rss-lapsuuden-rakentajat-podcast
rss-sosiopodi