ICLR: The AI conference that helped redefine the field

Amazon’s Stefano Soatto on how learning representations came to dominate machine learning.

The International Conference on Learning Representations (ICLR), which will be virtual this year and begins next week, is only eight years old. But according to Google Scholar’s rankings of the highest-impact publication venues in the field of AI, it’s second only to the enormously popular NeurIPS.

“That is quite impressive for a young conference,” says Stefano Soatto, the director of applied science for Amazon Web Services’ AI applications, who is on leave from the University of California, Los Angeles, where he’s a professor of computer science.

“ICLR was born as a niche conference but has become the mainstream,” Soatto explains. “It is specifically a conference on learning representations. Representations are functions of the data that are designed or learned so as to solve a given task. Because powerful data representations have been so central — thanks to the advent of deep learning — the difference between ICLR and the other AI conferences has shrunk.”

Stefano Soatto.png
Stefano Soatto, director of applied science for Amazon Web Services’ AI applications
Credit: UCLA Samueli

Originally, Soatto explains, developing data representations required expertise in the relevant fields. For example, he says, consider SIFT, or the scale-invariant feature transform. As its name suggests, SIFT produces representations of visual features that are invariant with respect to scale: the features that characterize images of dogs, for example, should be the same whether the dog is photographed in long shot or closeup.

“SIFT comes from two disciplines that have deep roots,” Soatto says. “One is harmonic analysis — all the literature on wavelets, filter banks, multiscale Fourier analysis, and so forth. The other is computational neuroscience, where, going back to Marr, people have noticed there is a certain organization in the processing of data in the visual cortex. So SIFT is kind of the summa sensible implementation of ideas from neuroscience and harmonic analysis that really required specific domain knowledge.

“But then neural networks come about, and with relatively simple operations from linear algebra and optimization, all of a sudden you could obtain results that are state of the art. So that was really a game changer.”

“I’m not suggesting that neural networks are easy,” he adds. “You need to be an expert to make these things work. But that expertise serves you across a broader spectrum of applications. In a sense, all of the effort that previously went into feature design now goes into architecture design and loss function design and optimization scheme design. The manual labor has been raised to a higher level of abstraction.”

Versatility

Two of the four Amazon papers at ICLR are on the topic of meta-learning, or learning how to learn, and the other two are on transfer learning, or improving a network’s performance in a domain where data are sparse by pre-training it on a related domain where data are abundant. But all four papers are about adapting machine learning systems to new tasks.

This is natural, Soatto says, given the current state of the field of learning representations.

“If you ask the question, ‘Given a particular set of data and given a task, what is the best possible representation one could construct?’, we have a good handle on that, both theoretically and practically,” Soatto says. “What remains a challenge are two complementary problems. One is, ‘Given a task, what is the best data I can get for it?’ That’s the problem of active learning, which Amazon Web Services is covering with Ground Truth, autoML, and Custom Labels.”

“The other is when you want to use a model trained for a particular learning task on a different task,” Soatto continues. “This is the problem of transfer learning and domain adaptation, where you know that your training set will be misaligned from the test sets.” It’s also the problem that the three ICLR papers from Soatto’s group at Amazon address.

Benchmarks

“‘A Baseline for Few-Shot Image Classification’ speaks to the gap between academic research and real-world research,” Soatto says. “There is a field called few-shot learning. The idea is, basically, you want to learn how to solve learning tasks given very few samples. And there are some benchmark data sets.

“Benchmarks are a sanity check that allows you to objectively compare with others. But sometimes the benchmarks are detrimental to progress because they incentivize playing to the benchmark, developing algorithms that do well on the benchmarks.

“When we started looking at few-shot learning, we noticed that the benchmarks are very strange in the sense that they force you to make specific choices of how many images you train with: either one or five. But if we have a service for few-shot learning — which we do, called Custom Labels — people bring in however many images they have. It could be a million; it could be a hundred; it could be ten; it could be one.

“Obviously, you’re not going to be able to serve a different model for every possible number of samples they bring. So what we said was, ‘Why don’t we try the simplest thing that we can think of that would work no matter what the few-shot conditions?' — with the expectation that this would be a baseline, the first thing that you can think of and easily implement that everybody should beat.

“And to our surprise, this trivial baseline beat every top-performing algorithm. Obviously, the paper is not saying this is how you should solve few-shot learning. It’s saying that we should rethink the way we evaluate few-shot learning, because if the simplest possible thing you can think of beats the state of the art, then there’s something wrong with the way we’re doing it.”

“We are at a time in history where industry leads academia, in the sense that it defines problems that just by sitting in your office and thinking of cool things to work on would not emerge,” Soatto adds. “These papers offer some examples, but there are many others.”

About the Author
Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor at MIT Technology Review and the computer science writer at the MIT News Office.

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Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about using econometrics to make multi-million dollar decisions more Science and Data Driven? Are you interested in supporting Consumer Hardware device concepts from innovative idea inception to launch? Do you want to work on a Economics and Data Science team focused on tackling some of the hardest business questions within the Devices business at Amazon and then scaling those Statistics and Econometrics solutions via internal to Amazon tools? Then this could be the role for you!Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Amazon Devices and Services team is the area of Amazon focused on inventing platforms that delight customers by eliminating friction they have in supplying, entertaining, and managing the home and beyond.The Device Economics team owns demand estimates and pricing recommendations of concept devices before customers know they exist. We support over 100 device-specific analyses a year on hardware and services ranging from Echo Frames to Kindle Paperwhite to Blink Video Camera subscriptions to the Amazon Smart Plug…all prior to launch.. We are a cross-functional Product team working to scale Econometrics through Amazon and beyond by incorporating Science into internal facing tools and making it easier for others to do so as well.In this role, you will support up to senior leadership decision meetings around approving confidential funding requests (PRFAQs) for brand new devices and services, build decisions around how many hardware devices to manufacture prior to receiving any customer signal, and pricing decisions around how to price and promote products and services. You will leverage Science and Tools produced by the Device Economics team such as conjoint demand models to produce these recommendations. As part of the stakeholder-facing arm of the team, you will own relationships with decision makers to help improve the end-customer experience by making the decisions that impact those end-customers more data and Science-driven. In parallel, you will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. You will own projects to make progress on Decision Science itself. Through this all, we will invest in your development to pursue your career goals.We are willing to consider L5 candidates across the BA/BIE job families where we'll bar raise your Science skills.
US, WA, Seattle
Job summaryAlexa Smart Home Research is looking for a brilliant quantitative researcher to drive a new program to ensure Alexa always delivers a four star experience. In this role, you will define the roadmap for the SH segmentation program, create experiments to evaluate customer behavior and sentiment that drive these higher quality experiences for our target customers. These insights help Alexa Smart Home Marketing and Product teams make data driven decisions about our marketing and product strategies ensuring products are accurately conveyed, appropriately priced and designed, and with each launch we are moving the needle for customers to help them accomplish their ideal smart home.Key job responsibilities· Identify and propose key opportunities for improving the product development and marketing strategy for Alexa products· Develop and execute research projects, including leading all project phases: methodology and study design, data gathering and manipulation, analysis, interpretation and presentation of results· Lead and execute validation and impact studies· Define project requirements, document business and functional specifications, map current and future state business processes· Build automated mechanisms for evaluating, measuring, and deploying the algorithms and/or models you develop.· Bring a deep level of expertise in one of the Research Marketing disciplines (e.g. Statistics)A day in the lifeAs part of your work, you will lead quantitative research projects that build our understand of smart home customers, identifying what works well and areas of improvement for Alexa Smart Home that will ensure we continue to delight our customers. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should be able to work with business customers in understanding the business requirements and research impact.About the teamWe are responsible for UX and market research (foundational, market fit, usability and concept testing), Beta launch readiness and voice of the customer. These services product org-wide customer insights that help SH teams connect directly with customers daily, supporting the end-to-end product readiness, and look around the corner to understand customer and competitor trends.
US, CA, Irvine
Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about cutting-edge deep-learning NLP, NLU, and Conversational AI? If so, then come and join the Alexa Artificial Intelligence (AI) team. We are the science team behind Amazon’s intelligence voice assistance system and are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.Key job responsibilitiesAs an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the teamWe are a science and engineering team part of Alexa AI organization. Our mission is to help Alexa decide which action to take in response to customer requests, incorporating a variety of contextual signals including both direct and indirect customer feedback to provide the best response to the customer. Our work directly contributes to improvement in Alexa business and customer metrics.