Well File:

Inductive Learning

No gas rebates in sight as average prices in L.A. barrel toward $6 a gallon -- again

Los Angeles Times

Experts say a perfect storm of supply-and-demand issues are sending gas prices in Los Angeles soaring again, with the price-per-gallon increasing more than 14 cents in the last 16 days, according to the latest fuel prices tracked by AAA. L.A. fuel prices are again inching toward a $6-a-gallon record set in March. The average price of a gallon of regular gasoline in the Los Angeles area is currently $5.91, with plenty of stations charging well over that. A year ago the price was $4.16. Overnight, the price jumped 2.2 cents, the highest level it has risen since February.

Director, Data Engineering


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Graph Machine Learning with Python Part 4: Supervised & Semi-Supervised Learning


This story will explore how we can reason from and model graphs using labels via Supervised and Semi-Supervised Learning. I'm going to be using a MET Art Collections dataset that will build on my previous parts on Metrics, Unsupervised Learning, and more. Be sure to check out the previous story before this one to keep up on some of the pieces as I won't cover all concepts again in this one: The easiest approach to conduct Supervised Learning is to use graph measures as features in a new dataset or in addition to an existing dataset. I have seen this method yield positive results for modeling tasks, but it can be really dependent on 1. how you model as a graph (what are the inputs, outputs, edges, etc.) and 2. which metrics to use. Depending on the prediction task, we could compute node-level, edge-level, and graph-level metrics.

Congratulations to the 2022 ICLR outstanding paper award winners!


The winners of the 2022 International Conference on Learning Representations (ICLR) outstanding paper awards have been announced. There are seven outstanding paper winners and three honourable mentions. The award winners will be presenting their work at the conference, which is taking place virtually, this week. Analytic-DPM: an analytic estimate of the optimal reverse variance in diffusion probabilistic models Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang Abstract: Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate over thousands of timesteps.

Self-Supervised Learning in Machine Learning


Self-supervised learning (SSL) is gaining a larger foothold in the world of machine learning (ML). As learning models are refined and expanded, machines that teach themselves, understand context and are able to fill in the blanks where there are holes in the information are the next step. Machines are taught to analyze, predict and advise on possible outcomes. Supervised learning - Practitioners train the machine on inputs paired with labelled outputs, teaching it to make associations. Example: A shape with three sides is labelled triangle .

#258 - Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning


Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the seminal researchers in the history of machine learning. Please support this podcast by checking out our sponsors: – Public Goods: https://publicgoods.com/lex and use code LEX to get $15 off – Indeed: https://indeed.com/lex Books and resources mentioned: Self-supervised learning (article): https://bit.ly/3Aau1DQ SUPPORT & CONNECT: – Check out the sponsors above, it's the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman On some podcast players you should be able to click the timestamp to jump to that time.

Maple Leafs beat Islanders, break team wins and points marks

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Jack Campbell made 27 saves and the Toronto Maple Leafs set franchise records for victories and points, beating the New York Islanders 4-2 on Sunday night without NHL goals leader Auston Matthews. William Nylander, Mitch Marner, Pierre Engvall and David Kampf scored to help Toronto improve to 50-20-6 and reach 106 points. "We've got a great group in here," Campbell said after the record-setting win.

Maple Leafs rout Capitals, close in on team victory mark

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. William Nylander, Michael Bunting and Ilya Mikheyev each scored twice, Auston Matthews had two assists to reach 101 points and the Toronto Maple Leafs routed the Washington Capitals 7-3 on Thursday night. Ilya Lyubushkin also scored and captain John Tavares had four assists to help the Maple Leafs improve to 48-20-6, a victory shy of the club record set in 2017-18. Matthews -- with 58 goals and 43 assists -- became the third player in Toronto history with 100 or more points.

Astronaut Mark Vande Hei on his record-breaking spaceflight and adjusting to life on Earth

PBS NewsHour

Geoff Bennett: On March 30, NASA's Mark Vande Hei stepped foot on Earth for the first time in nearly a year. Earlier this week, I talked to the astronaut about his record-setting journey. Geoff Bennett: NASA Astronaut Mark Vande Hei set a new record for the single longest spaceflight by an American, spending 355 days in orbit, and surpassing the record held by retired Astronaut Scott Kelly. When you left for this flight, did you know it would last that long? Mark Vande Hei, Nasa Astronaut: Jeff, I did not know it was ñ it would last that long.

Event Log Sampling for Predictive Monitoring


Predictive process monitoring is an exceedingly active field of research. At its core, the fundamental component of predictive monitoring is the abstraction technique it uses to obtain a fixed-length representation of the process component subject to the prediction (often, but not always, process traces). In the earlier approaches, the need for such abstraction was overcome through model-aware techniques, employing process models and replay techniques on partial traces to abstract a flat representation of event sequences. Such process models are mostly automatically discovered from a set of available complete traces, and require perfect fitness on training instances (and, seldomly, also on unseen test instances).