This MLconf has been our largest event yet! In effort to keep the intimate feel of MLconf, we’ve opted to keep attendance limited to 500 or less for each event. So far, we’ve found this to keep the personality and experience we’ve been hoping for. Overall, I have to say that this MLconf was the most successful one, so far. In terms of organization, things seemed to flow smoothly. Audio/Visual worked surprisingly well, including the streaming of the event. I remember last year 5 minutes before the morning keynote all the electronics were down!!! We were grateful to not have that stress this year. We did experience one unexpected glitch during lunch- the venue did not have enough waiters to cater the crowd and we, the organizers, had to rush into the kitchen and start serving food! I can’t complain- it was a fun experience.. All hands on deck!
The morning track had a theme of deep learning and tensor flow. Alex Smola, CEO at Marianas Labs, gave an impressive keynote with tricks, algorithms, and facts about mainstream algorithms; and of course- about deep learning. The second track, by Braxton McKee, CEO at Ufora, gave a different angle on parallelization and scaling. Following a programing language/compiler approach he emphasized on an automatic way where all the details about cache efficiency etc are taken care by machine learning algorithms that optimize data distribution under the hood. Ufora chose MLconf to announce the open sourcing of their platform. Taking a small break from platforms, Isabelle Guyon, President at ChaLearn, emphasized the problem of inferring causal factors in everyday data science. The competition she ran, revealed an interesting approach discovered by someone who happened to be in the audience! The first session ended with the room being packed, it was impossible to find a spot even to stand.
Right after the break we announced the winners of the MLconf Industry Impact Student Research Award, sponsored by Google. The winners were: Furong Huang, Student at UC Irvine, and Virginia Smith, Student at UC Berkeley. Find out more about their work in our blogpost. Following their brief summaries of their work, we introduced Quoc Le, Software Engineer at Google. Quoc presented on the recent advances in deep learning at Google, that raised philosophical issues about the meaning of life!
Irina Rish, Research Staff at IBM Watson, presented the brain, schizophrenia, FMRI, EKG while demonstrating a live and stylish EKG sensor. That was the best demonstration of sparse learning in action. Following Irina, Alison Gilmore, Data Scientist at Ayasdi, presented on the fascinating world of topological learning and showed how it can be applied in data analysis. As she presented, it’s all about finding the shape of the data. Just before going for lunch, Subutai Ahmad, VP of Research at Numenta, announced the open sourcing of their anomaly detection for streaming data.
During the break a loop of videos by Welch Labs presented the fundamentals of neural networks. Welch labs offers a very nice set of videos for machine learning in a user friendly way. It is not education, it is edutainment!
Following lunch, MLconf veteran speaker, Xavier Amatriain, VP of Engineering at Quora, gave us another 10 lessons he learnt from machine learning. Last year, he gave us his first 10 lessons. Both of his talks turned out to be total crowd favorites! Another presentation of lessons learned followed after Xavier; from Ben Hamner, CTO at Kaggle. Apparently this was the lesson’s session, which also included Justin Basilico, Research/Engineering Manager, at Netflix, who spoke about the lessons he learned from recommender systems. The session ended with a different type of recommendations- Brad Klingenberg, the Director of Styling Algorithms at Stitch Fix, reminded us that the human element is very important in recommendations for fashion and still more reliable than the machine.
Anima Anandkumar, from UC Irvine, presented on the application of tensors in a practical model. It is amazing what this simple model can do. Guaranteeing global optimality is a big plus. Following Anima, Alessandro Magnani, Data Scientist at Walmart Labs, presented a problem which is becoming hot these days- recommending items with short lifestyle! The last two talks by Naraynan Sundaram, Research Scientist at Intel, and Melanie Warrick, Deep Learning Engineer at Skymind kept the attendance at very high levels although it was already late. Narayan’s talk was the only one this time about graphs. The platform they develop at Intel seems promising and very fast. Melanie closed the conference with the favorite subject, attention models. It was a nice presentation for a subject that was mentioned briefly by Quoc earlier in the morning.
We want to thank the speakers for devoting considerable time to prepare their material and present it. If you want to get a glance of MLconf and test how much you understand the content, feel free to take our quiz. Video footage and slides from all the talks can be found on the event page.
Call for Abstracts, MLconf NYC 2016
MLconf is a premier conference in machine learning for the industry. The proposed talks cover the following areas
- Algorithms that have graduated academic conferences such as NIPS,ICML, etc and have proven to be effective, robust and scalable in production
- Novel datascience practices, such as data transformations, new data sources, novel representations, etc
- Machine Learning/Data science case studies (Lessons learned), demonstrating challenges in the wild and how to handle them in a new way
- New platforms, tools for machine learning. Emphasis should be given on the technical challenges, benchmarks and motivation for the development
- New business practices, for managing, growing data science teams and expanding machine learning to new domains
- Tutorials and novel ways of presenting and simplifying machine learning domains, including: deep learning, kernel methods, bayesian nonparametrics, tensor algebra, etc
- Up and coming areas of machine learning that you think will dominate the industry in the future, such as probabilistic programming etc.
Submit your abstracts or draft presentations by February 4th. The selection of the presentation will be based on:
- Clarity and novelty of the presentation
- Diversity of the topics for the conference
- Speaker’s experience and reputation in the industry
- Limit your abstract to 500 words
- Try to submit a draft of your presentation by February 4th. This will make the work of the reviewers easy as it will help us understand the topic better as well as your ability to present the subject.
- Feel free to include supplementary material such as video/audio clips as well as reference papers. Put them all in a zip file and upload them
- Along with your presentation submit 5 multiple choice questions that would test the attendees comprehension of your presentation. MLconf offers a quiz after the day of the event that certifies attendee’s comprehension of the conference
Some thoughts about MLconf ATL
The day after an MLconf is always very relieving. We can’t resist thinking though about what went well and what went wrong. I have to say that there were some unforeseen last minute changes- We had a speaker unable to participate because of a car accident (don’t worry he is ok now), a withdrawal of a talk (yes sometime people realize their results are different than expected) and two other companies had to change their speaker because of internal obligations. On top of that there was technical problem with the badges the day before an AV black out 10 minutes before the beginning of the conference. Additionally, the well-anticipated books by Pedro Domingos arrived the day after the event! (Thanks UPS!!). Thanks to the titanic effort of Courtney, Tyler and the rest of the crew, most of these glitches were fixed and did not affect the quality of the event!
On the other hand there were some last minute additions to the program that had huge success. Our collaboration with Welch Labs that was agreed a few days prior to the event was well received and audience loved the short tutorial videos. We noticed that 100% of the attendees prefered to delay their lunch or coffee in order to watch the videos. Also the last minute participation of the champion robotics team from Grady High School gave us some nice moments during the lunch break.
The feedback from the audience was very encouraging and we feel like we met the expectations in terms of the content and the experience. Learning from our experiences, we are thrilled to host the next event in San Francisco November 13th with a great line-up of speakers! Readers of this blog mention “Blog15” and save 15% on tickets to MLconf SF from now until 10/01/15!
"MLconf Industry Impact Student Research Award" Sponsored by Google
Google is now sponsoring a new MLconf award program called the MLconf Industry Impact Student Research Award. This November, our committee of distinguished ML professionals will award a student whose research is deemed to be useful for solving the problems currently faced in industry. Last year, we started the tradition of including academic presentations in our events that we felt were relevant to current industry application. One example of this included a talk at MLconf Atlanta, on VoG, an approach that efficiently summarizes large graphs by finding their most interesting and semantically meaningful structures, by Danai Koutra, in her last year as PhD student at Carnegie Mellon. We’re now moving forward with an award for such research.
We need your input!
In our efforts to identify potential nominees, our program committee would like you, our respected leaders from the MLconf community, for recommendations of students that are participating in this instrumental research!
The winner of this contest will be announced at onstage at MLconf San Francisco on Friday, November 13th, 2015. Following the announcement, the winner will be invited to present their work at MLconf in New York City on 04/15/16.
Do you know someone that you’d like to nominate? Click here!
Thanks for participating!
-Courtney, MLconf
KDnuggets Report
This week, a summary for kdnuggets.com about MLconf, and what the industry leaders are saying about machine learning. We are so excited to not only be featured on KDnuggets, but ecstatic about the speakers we’ve been able to and plan to host in the machine learning community.
Covering quite a bit of info in his short report. Every year our conference is growing, and we are working hard to ensure that the industries leaders, and the smallest start-ups leave with more knowledge than they came in with. At the very least, we want attendees to leave with new tools and methodologies to consider, and a whole new set of questions about Machine Learning and Data Sciences.
In this post, it also discusses the different topics we cover at MLconf. From new algorithms, to scaling, to deep learning, and everything in-between, MLconf is providing an educational experience for everyone. If you want to check out the report, click the link at the bottom of the post, and be sure to grab your tickets to the MLconf nearest you in 2015!
Of course we want to thank our sponsors for making every MLconf possible, and giving us the ability to grow with machine learning each year.
We look forward to learning with you.
Click here for the feature!
Data Elixir Interviews MLconf
Our past Technical Chair and I recently spoke with Lon Riesberg, of Data Elixir, regarding The Machine Learning Conference. As MLconf considers itself a community event, we found Data Elixir to be a great newsletter/information hub for the machine learning community. Check out our conversation below!
Lon: Why did you start MLconf?
Courtney: The Machine Learning Conference (MLconf) began in 2012, as a partnership with Carnegie Mellon University’s GraphLab team, to gather the thought leaders in Machine Learning, specifically Graph Databases. In 2013, MLconf became a separate event, devoted to the Machine Learning and Data Science community in San Francisco, agnostic of any tool, platform or company. MLconf events host speakers from various industries, research and universities to discuss recent research and application of Machine Learning methodologies and practices. In 2014, MLconf entered NYC and Atlanta, as well as San Francisco. In 2015, MLconf will host conferences in NYC, Atlanta, Seattle and San Francisco, with plans to enter additional US cities in 2016, and the UK.
Lon: Who’s the ideal attendee?
Technical Chair: We usually try to answer the opposite question; how can we be ideal for people who want to keep in touch with what is happening in machine learning. From our registration statistics (yes we also do machine learning) we see that our attendees are data scientists focused on the machine learning aspect, engineers or Phds who are transitioning to datascience, grad students and some executives/Venture Capitalists. Attendees typically attend MLconf, to enjoy a quick, one-day conference that includes talks from companies like Google, Yahoo, Netflix, etc or other upcoming startups that present on what algorithms, methods and tools have already been tested and proved efficient.
Lon: Who are the presenters? (e.g. typical backgrounds, some well-known presenters)
Technical Chair: The presenters are Research Scientists/Engineers and Professors. Some of them are machine learning veterans like Corinna Cortes from Google Research as well as influencers like Claudia Perlich with industrial and academic backgrounds. MLconf events also host speakers with strong experience in complicated machine learning engineering implementations like Xavier Amartian from Quora (formerly Netflix). The MLconf program committee searches for people who have an interesting track record in machine learning and interesting topics to present. We spend a lot of time working with the presenters to make sure that the math level the style and the content meets the expectation of the audience. We absolutely do not allow presentations that are sales and marketing pitches, or make strong arguments that are disrespectful for the community. Every presentation should have a clear message, not heavy in mathematical details and references for further and deeper study if the audience wants to learn more.
Lon: What can attendees expect to get out of MLconf?
Courtney: MLconf offers busy professionals a quick and digestible day of presentations to help stay current on the application of machine learning tools and techniques within the community. Grad students may find certain presentations that influence their direction in their studies, they will learn about new tools/software and, of course, network with companies that are hiring. MLconf offers students a glance at what the Machine Learning methods are used in industry and how to pursue such professional experience within hiring organizations. Academic Professors seek collaboration to the industry and they often evangelize new algorithms and paradigms accompanied by open source software. Additional attendees benefit from the sharing of knowledge and networking during the extended lunch break and coffee breaks.
Lon: What do you hope for the future of MLconf?
Courtney: As I mentioned before, we want to cross the US borders and have MLconf hosted in European cities. In terms of the content and the format of the conference we are experimenting with new features. Last year we added the book exhibition where publishers contribute several ML books. This year we are adding the short talks that answer technical questions on machine learning topics. This year we launched a job search section on our website. In this free service, our goal is to smoothly and confidentially connect interested attendees with hiring companies within the community.