No head left behind - Multi-head alignment distillation for transformers

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Knowledge distillation aims at reducing model size without compromising much performance. Recent work has applied it to large vision-language (VL) Transformers, and has shown that attention maps in the multi-head attention modules of vision-language Transformers contain extensive intra-modal and cross-modal co-reference relations to be distilled. The standard approach is to apply a one-to-one attention map distillation loss, i.e. the Teacher’s first attention head instructs the Student’s first head, the second teaches the second, and so forth, but this only works when the numbers of attention heads in the Teacher and Student are the same. To remove this constraint, we propose a new Attention Map Alignment Distillation (AMAD) method for Transformers with multi-head attention, which works for a Teacher and a Student with different numbers of attention heads. Specifically, we soft-align different heads in Teacher and Student attention maps using a cosine similarity weighting. The Teacher head contributes more to the Student heads for which it has a higher similarity weight. Each Teacher head contributes to all the Student heads by minimizing the divergence between the attention activation distributions for the soft-aligned heads. No head is left behind. This distillation approach operates like crossattention. We experiment on distilling VL-T5 and BLIP, and apply AMAD loss on their T5, BERT, and ViT sub-modules. We show, under vision-language setting, that AMAD outperforms conventional distillation methods on VQA-2.0, COCO Captioning, and Multi30K translation datasets. We further show that even without VL pre-training, the distilled VLT5 models outperform corresponding VL pre-trained VL-T5 models that are further fine-tuned by ground-truth signals, and that fine-tuning distillation can also compensate to some degree for the absence of VL pre-training for BLIP models.
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ES, M, Madrid
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US, WA, Seattle
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IN, KA, Bangalore
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US, NJ, Newark
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US, CA, Sunnyvale
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US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA | Sunnyvale, CA, USA
US, WA, Seattle
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US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians on a mission to develop a fault-tolerant quantum computer. You will be joining a team located in Pasadena, CA that conducts materials research to improve the performance of quantum processors. We are looking to hire a Quantum Research Scientist who will apply their expertise in materials characterization to the optimization of fabricated superconducting quantum devices. In this role, you are expected to lead and assist research projects that are aligned with our Center’s technical roadmap. You will develop new ideas and design experiments aimed at identifying the most promising material systems, characterization techniques, and integration processes for superconducting circuit applications. Key job responsibilities - Conduct experimental studies on the fundamental properties of superconducting, semiconducting, and dielectric thin films - Develop and implement multi-technique materials characterization workflows for thin films and devices, with a focus on the surfaces and interfaces - Work closely with other research scientists on the Materials team to develop material processes directed toward optimizing thin film properties, controlling the surface chemistry and morphology, and impacting device performance - Identify materials properties (chemical, structural, electronic, electrical) that can be a reliable proxy for the performance of superconducting qubits and microwave resonators - Communicate engineering and scientific findings to teammates, the broader CQC and, when appropriate, publish findings in scientific journals A day in the life AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. About the team Our team contributes to the fabrication of processors and other hardware that enable quantum computing technologies. Doing that necessitates the development of materials with tailored properties for superconducting circuits. Research Scientists and Engineers on the Materials team operate deposition and characterization systems in order to develop and optimize thin film processes for use in these devices. They work alongside other Research Scientists and Engineers to help deliver fabricated devices for quantum computing experiments. We are open to hiring candidates to work out of one of the following locations: Pasadena, CA, USA
US, CA, Sunnyvale
Help re-invent how millions of people watch TV! Fire TV remains the #1 best-selling streaming media player in the US. Our goal is to be the global leader in delivering entertainment inside and outside the home, with the broadest selection of content, devices and experiences for customers. Our science team works at the intersection of Recommender Systems, Information Retrieval, Machine Learning and Natural Language Understanding. We leverage techniques from all these fields to create novel algorithms that allow our customers to engage with the right content at the right time. Our work directly contributes to making our devices delightful to use and indispensable for the household. Key job responsibilities - Drive new initiatives applying Machine Learning techniques to improve our recommendation, search and entity matching algorithms - Perform hands-on data analysis and modeling with large data sets to develop insights that increase device usage and customer experience - Design and run A/B experiments, evaluate the impact of your optimizations and communicate your results to various business stakeholders - Work closely with product managers and software engineers to design experiments and implement end-to-end solutions - Setup and monitor alarms to detect anomalous data patterns and perform root cause analyses to explain and address them - Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences - Help attract and recruit technical talent; mentor junior scientists We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA