Learning computational tasks from single examples

New “meta-learning” approach improves on the state of the art in “one-shot” learning.

In the past decade, deep-learning systems have proven remarkably successful at many artificial-intelligence tasks, but their applications tend to be narrow. A computer vision system trained to recognize cats and dogs, for instance, would need significant retraining to start recognizing sharks and sea turtles.

Meta-learning is a paradigm intended to turn machine learning systems into generalists. A meta-learning model is trained on a range of related tasks, but it learns not only how to perform those tasks but also how to learn to perform them. The idea is that it could then be adapted to new tasks with only a handful of labeled training examples, drastically reducing the need for labor-intensive data annotation.

At the (virtual) International Conference on Learning Representations, we will present an approach that improves performance on meta-learning tasks without increasing the data annotation requirements. The key idea is to adapt the meta-learning procedure so that it can leverage small sets of unlabeled data, in addition to the traditional labeled examples.

Meta-learning
In meta-learning, a machine learning model learns how to learn. During meta-training, the model is trained on a group of related tasks — using data from “support sets” — and tested using data from “query sets”. But the query sets are labeled, so the model can assess how effectively it's learning. During meta-testing, the model is again trained on a group of support sets, but it's evaluated on its ability to classify unlabeled query data.
Stacy Reilly

The intuition is that even without labels, these extra data still contain a lot of useful information. Suppose, for instance, that a meta-learning system trained on images of terrestrial animals (such as cats and dogs) is being adapted to recognize aquatic animals. Unlabeled images of aquatic animals (i.e., images that don’t indicate whether an animal is a shark or a sea turtle) still tell the model something about the learning task, such as the lighting conditions and background colors typical of underwater photos.

In experiments, we compared models trained through our approach to 16 different baselines on an object recognition meta-learning task. We found that our approach improved performance on one-shot learning, or learning a new object classification task from only a single labeled example, by 11% to 16%, depending on the architectures of the underlying neural networks.

Meta-learning

In conventional machine learning, a model is fed a body of labeled data and learns to correlate data features with the labels. Then it’s fed a separate body of test data and evaluated on how well it predicts the labels for that data. For evaluation purposes, the system designers have access to the test-data labels, but the model itself doesn’t.

Meta-learning adds another layer of complexity. During meta-training — the analogue of conventional training — the model learns to perform a range of related tasks. Each task has its own sets of training data and test data, and the model sees both. That is, part of its meta-training is learning how particular ways of responding to training data tend to affect its performance on test data.

During meta-testing, it is again trained on a range of tasks. These are related to but not identical to the tasks it saw during meta-training — recognizing aquatic animals, for instance, as opposed to terrestrial animals. Again, for each task, the model sees both training data and test data. But whereas, during meta-training, the test data were labeled, during meta-testing, the labels are unknown and must be predicted.

The terminology can get a bit confusing, so meta-learning researchers typically refer to the meta-learning “training” sets as support sets and the meta-learning “test” sets as query sets. During meta-training, the learning algorithm has access to the labels for both the support sets and the query sets, and it uses them to produce a global model. During meta-testing, it has access only to the labels for the support sets, which it uses to adapt the global model to each of the new tasks.

Our approach has two key innovations. First, during meta-training, we do not learn a single global model. Instead, we train an auxiliary neural network to produce a local model for each task, based on the corresponding support set. Second and more important, during meta-training we also train a second auxiliary network to leverage the unlabeled data of the query sets. Then, during meta-testing, we can use the query sets to fine-tune the local models, improving performance.

Leveraging unlabeled data

A machine learning system is governed by a set of parameters, and in meta-learning, meta-training optimizes them for a particular family of tasks — such as recognizing animals. During meta-testing or operational deployment, the model uses a handful of training examples to optimize those parameters for a new task.

A particular set of parameter values defines a point in a multidimensional space, and adaptation to a new task can be thought of as searching the space for the point representing the optimal new settings.

Meta-learning parameter space
In traditional meta-learning (left), the result of training is a model (φ) that can be adapted to a new set of related tasks (1 – 4). Adaptation involves searching for the optimal settings 1 – θ4) of the model parameters, based on a small set of labeled data (dl1 – dl4). Our system (right), by contrast, uses the labeled data and the available unlabeled data (x1 – x4) to better approximate those settings.

A traditional meta-learning system might begin its search at the point defined by the global model (φ in the figure above); this is the initialization step. Then, using the labeled data of the support set, it would work its way toward the settings that correspond to the new task; this is the adaptation step.

With our approach, by contrast, the initialization network selects a starting search location on the basis of the data in the support set 01(dl1) – θ04(dl4) in the figure above). Then it works its way toward the optimal settings using the unlabeled data of the query set (x1 – x4, above). More precisely, the second auxiliary neural network estimates the gradient implied by the query set data.

In the same way that the parameter settings of a machine learning model can be interpreted as a point in a representational space, so can the parameter settings and the resulting error rate on a particular data set. The multidimensional graph that results is like a topological map, with depressions that represent low error rates and peaks that represent high error rates. In this context, machine learning is a matter of identifying the slope of a depression — a gradient — and moving down it, toward a region of low error.

This is how many machine learning systems learn, but typically, they have the advantage of knowing, from training data labels, what the true error rate is for a given set of system parameters. In our case, because we’re relying on the unlabeled query set data, we can only guess at the true gradients.

That’s where the second auxiliary neural network comes in: it infers gradients from query set data. The system as a whole then uses the inferred gradients to fine-tune the initial parameter settings supplied by the first neural network.

The approach can be explained and justified through connections to two topics in theoretical machine learning, namely empirical Bayes and information bottleneck. These theoretical developments are beyond the scope of this blog post, but the interested reader can consult the full manuscript.

The associated software code has also been open-sourced as part of the Xfer repository.

Although our system beat all 16 baselines on the task of one-shot learning, there were several baseline systems that outperformed it on five-shot learning, or learning with five examples per new task. The approaches used by those baselines are complementary to our approach, and we believe that combining approaches could yield even lower error rates. Going forward, that’s one of several extensions of this work that we will be pursuing.

Research areas
About the Author
Pablo Garcia Moreno is an applied scientist in the Alexa Shopping organization.
About the Author
Andreas Damianou is a senior applied scientist in the Alexa Shopping organization.

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Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. These are exciting fast-paced businesses in which work on extremely interesting analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant
US, NY, New York
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. In this role, you will be designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.We’re looking for talented data scientists capable of applying classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team.The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high‐judgement decision‐making where we cannot apply automation.
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team.The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high‐judgement decision‐making where we cannot apply automation.
US, CA, San Francisco
LOCATION: San Francisco, CAMULTIPLE POSITIONS AVAILABLE1. Analyze real user data (search query logs) using SQL or equivalent data query language.2. Train machine learning / deep learning based models using ML platforms and libraries such as Tensorflow, Pytorch, Pyspark etc.3. Apply natural language processing techniques to improve ranking of search results and develop new ranking features and techniques building upon the latest results from the academic research community4. Boost search conversion by classifying user search queries and recommending relevant content5. Contribute to operational excellence in search team's scientific features, constructively identifying inefficient processes and proposing solutions6. Experiment with different models, analyze results using statistical methods and iterate on improving the results7. Propose and validate hypotheses to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.8. Design, develop, and implement production level code that serves millions of search requests. Own the full development cycle: design, development, impact assessment, A/B testing (including interpretation of results) and production deployment.9. Telecommuting benefits available#0000
US, CA, Pasadena
LOCATION: Pasadena, CAMULTIPLE POSITIONS AVAILABLE1. Assist large enterprises with researching and learning about new technologies in cloud computing. Understand their business needs in different industries and guide them to a solution using AWS Services.2. Develop approaches to industry problems in optimization, simulation and machine learning and execute customer projects and cases studies end-to-end.3. Develop a deep understanding of emerging technologies and innovate in co-designing novel algorithms on these platforms.4. Collaborate with AWS Services and research teams to continually improve the customer experience.5. Collaborate across the entire AWS organization to bring access to product and service teams, get the right solutions delivered and drive feature innovation based upon customer needs.6. Influence a team of scientists who are working on procedures to build quantum computers more reliably and develop methods to benchmark the performance of quantum hardware.7. Lead the exploratory research and prototyping of new schemes and simulation software for error correction resource estimates and benchmarking.8. Publish in scientific journals, create white papers, write blogs, and build demos and other reusable collateral that can be used by customers.9. Lead research and publication efforts focused on quantum error correction and quantum bench marking.10. Domestic and some international travel may be required up to 25% of the time.11. Telecommuting benefits available.#0000