Abstract
Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks. Current practices for fine-tuning typically involve selecting an ad-hoc choice of hyperparameters and keeping them fixed to values normally used for training from scratch. This paper re-examines several common practices of setting hyperparameters for fine-tuning. Our findings are based on extensive empirical evaluation for fine-tuning on various transfer learning benchmarks. (1) While prior works have thoroughly investigated learning rate and batch size, momentum for fine-tuning is a relatively unexplored parameter. We find that the value of momentum also affects fine-tuning performance and connect it with previous theoretical findings. (2) Optimal hyperparameters for fine-tuning, in particular, the effective learning rate, are not only dataset dependent but also sensitive to the similarity between the source domain and target domain. This is in contrast to hyperparameters for training from scratch. (3) Reference-based regularization that keeps models close to the initial model does not necessarily apply for “dissimilar” datasets. Our findings challenge common practices of fine-tuning and encourages deep learning practitioners to rethink the hyperparameters for fine-tuning.
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US, Virtual
Job summaryHow do you manage inventory when you don’t own it? How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? If these type of questions get your mind racing, we want to hear from you.Supply Chain Optimization Technologies (SCOT) optimizes Amazon’s global supply chain end to end and build systems to deliver billions of products to our customers’ doorsteps faster every year while saving hundreds of millions of dollars using science, machine learning, and scalable distributed software on the Cloud. FBA is an Amazon service for our marketplace third party sellers, where our sellers leverage our world-class facilities and provide customers Prime delivery promise on all their goods. SCOT has launched a new team called Fulfillment by Amazon (FBA) Automation & Optimization to focus on optimizing our third-party supply chain, and is in search to hire a Principal Economist.Key job responsibilities· Design and develop rigorous models to understand and assess third party sellers’ behaviors and experience, including causal impact of various Amazon inventory policies on their short-term and long-term performance.· Design and conduct experiments to validate theories and improve understanding of Amazon’s third party ecosystem.· Collaborate with product managers, scientists, and software developers to incorporate models into production processes and influence senior leaders.· Own the scientific vision and direction related to FBA Sellers.· Own all development phases of economic modeling, including defining key research questions, recommending measures, working with multiple data sources, evaluating methodology and design, executing analysis plans, and interpreting and communicating results· Effectively communicate econometric models to business teams and incorporate feedback into project analysis/modeling.About the teamSellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. Fulfillment By Amazon (FBA) enables Sellers to provide fast and efficient deliver to their customers using Amazon fulfillment services. In 2020, Sellers enjoyed strong growth using FBA shipping more than half of all products offered on Amazon. To our consumers, FBA provides a broad and diverse inventory of products from Books, Electronics and Apparel to Consumables and beyond with many of them available with 1-Day shipping. The FBA Inventory team within the Amazon Supply Chain Optimization Technology (SCOT) organization is in charge of defining and delivering fulfillment services to our Sellers by leveraging Amazon’s expertise in machine learning, inventory optimization, big data, and distributed systems to deliver the best inventory management experiences for our FBA Sellers. We work full stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making.
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale ?This is a unique opportunity to get in on the ground floor, shape, and build the next-generation of Amazon Search. We are looking for exceptional scientists and ML engineers who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization.Please visit https://www.amazon.science for more information
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale?· Can we compress an extremely large model to a small model with minimal accuracy loss?This is a unique opportunity to get in on the ground floor, shape, and build the next-generation of Amazon Search. We are looking for exceptional scientists and ML engineers who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization.Please visit https://www.amazon.science for more information
US, CA, Sunnyvale
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US, Virtual
Job summaryAlexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team builds services and tools through Machine Learning techniques to implement our policies to detect and mitigate sensitive content in across Alexa.We are looking for an experienced Principal Applied Science to build industry-leading technologies in attribute extraction, annotation, and sensitive content detection and interpretation across all languages, modal, and countries. A Principal Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP and Computer Vision related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon.Key job responsibilitiesA Principal Applied Scientist should have good understanding of NLP models (e.g. Bi-LSTM, BERT, and other transformer based models) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies.You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you.A day in the lifeYou will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation for worldwide coverage. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, model development, and solution implementation. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership.About the teamThe mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics.The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.Job responsibilities
US, WA, Virtual Location - Washington
Job summaryVoice-driven AI experiences are finally becoming a reality and Amazon’s Alexa voice cloud service and Echo devices are at the forefront of this latest technology wave. We deliver world-class products on aggressive schedules that are used every day, by people you know, in and about their homes. At the same time, we obsess about customer trust and ensure that we build products in a manner that maintains our high bar for customer privacy. We are looking for a passionate and talented Applied Scientist with experience in delivering production systems based on innovative research. This is a unique opportunity to play a key role in an exciting, fast growing business. You will be working on one of the world's most cutting edge customer experience and technology. You'll design and run experiments, research new algorithms, and find new ways of optimizing customer experience. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally.You should thrive in ambiguous environments that require to find solutions to problems that have not been solved before. You enjoy and succeed in fast paced environments where learning new concepts quickly is a must. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience building large-scale distributed systems to creating reliable, scalable, and high performance products. Your strong communication skills enable you to work effectively with both business and technical partners.You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history. Candidates can work in Arlington, VA OR Seattle, WA.
US, WA, Seattle
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SE, Stockholm
Job summaryCome build the future of entertainment with us.Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.We strive to be a fast-moving, creative, and high-impact organization, but we think it is equally important to be collaborative, supporting, and high-trust in the way we work. We want to come to work every day loving not only what we do, but who we have the privilege of working with. Come help us make all of this a reality.Key job responsibilitiesAs part of the Automated Excellence organization, the Automated Reasoning team applies deep and cutting-edge automated reasoning techniques to detect defects automatically in Prime Video’s core systems and device-level code. The tools we build are mission-critical to the software development and release cycle of many Prime Video engineering organizations, and will represent a huge step forward in the sophistication of our approach to automated Quality Assurance. Your work on this team will help us address a new dimension of scale our business faces as we deliver our applications on an ever-expanding set of client devices.A day in the lifeYou will have the opportunity to apply your deep knowledge of automated reasoning techniques, such as static analysis, formal verification, symbolic execution, etc., to concrete problems our product and engineering teams face on a daily basis. You will collaborate with team members to design and deliver enterprise-scale systems that will be used by both internal and external customers. You will have the opportunity to analyse and verify code to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. You will help set and continuously evolve a culture of innovation and curiosity that helps us find and solve our customers’ biggest problems.About the teamTo help a growing organization quickly deliver more features to Prime Video customers, Prime Video’s Automated Excellence organization is innovating on behalf of our global software development team consisting of thousands of engineers. We build services and utilities that make developer’s lives easier and more productive, and that help them deliver at higher levels of quality.
IE, D, Dublin
Job summaryAre you a MS or PhD student interested in a 2022 Applied Science Internship in the fields of Speech, Robotics, Computer Vision, or Machine Learning/Deep Learning?Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way?If this describes you, come join our research teams at Amazon. As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept.We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.
US, VA, Arlington
Job summaryThe AWS Human Resources Operations and Analytics organization is a critical piece of the AWS flywheel. We are the curators of people data for the industry leader in Cloud Computing. As pioneers in this space, we get to answer new and interesting problems in the People Analytics space, always at scale, and across a variety of business and technical leaders. Our data is sourced from a variety of internal and external sources. The work we do enables leaders to continue to make industry shaking decisions with the knowledge that they are doing so based on reliably sourced and responsibly secured data. We own systems and database environments which are built with reliability and security as the foundation on which balances accessibility, speed, scale, and insight generation. Our systems of self-service data today will quickly evolve into self-service insights in 2022 and beyond.Research Scientists on this team have end-to-end range and capabilities. They work closely with stakeholders to define key business needs and deliver on commitments, retrieve and aggregate data from multiple sources, and compile it into a digestible and actionable format. They also gather and use complex data sets across domains, work closely with product managers, and lead the development of key machine learning features from development to deployment in a cross-functional team.The successful candidate will create documents and share findings in line with scientific best practices for both technical and nontechnical audiences and occasionally present research result at internal and external conferences. They will also work closely with Amazon worldwide operations and the People, Experience, Technologies team to define key business objectives, metrics, and data science deliverables, as well as lead the development of key machine learning features from inception to production in an agile development environment.