[MINI] Exponential Time Algorithms
Data Skeptic24 Marras 2017

[MINI] Exponential Time Algorithms

In this episode we discuss the complexity class of EXP-Time which contains algorithms which require $O(2^{p(n)})$ time to run. In other words, the worst case runtime is exponential in some polynomial of the input size. Problems in this class are even more difficult than problems in NP since you can't even verify a solution in polynomial time.

We mostly discuss Generalized Chess as an intuitive example of a problem in EXP-Time. Another well-known problem is determining if a given algorithm will halt in k steps. That extra condition of restricting it to k steps makes this problem distinct from Turing's original definition of the halting problem which is known to be intractable.

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

Jaksot(601)

Video Recommendations in Industry

Video Recommendations in Industry

In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the inter...

26 Joulu 202538min

Eye Tracking in Recommender Systems

Eye Tracking in Recommender Systems

In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University,...

18 Joulu 202552min

Cracking the Cold Start Problem

Cracking the Cold Start Problem

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply ap...

8 Joulu 202539min

Designing Recommender Systems for Digital Humanities

Designing Recommender Systems for Digital Humanities

In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Te...

23 Marras 202536min

DataRec Library for Reproducible in Recommend Systems

DataRec Library for Reproducible in Recommend Systems

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems resea...

13 Marras 202532min

Shilling Attacks on Recommender Systems

Shilling Attacks on Recommender Systems

In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithm...

5 Marras 202534min

Music Playlist Recommendations

Music Playlist Recommendations

In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation syste...

29 Loka 202552min

Bypassing the Popularity Bias

Bypassing the Popularity Bias

15 Loka 202534min

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