Bayesian

80 results found
  • David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau
    ICLR 2021 Workshop on Neural Architecture Search
    2021
    In addition to the best model architecture and hyperparameters, a full AutoML solution requires selecting appropriate hardware automatically. This can be framed as a multi-objective optimization problem: there is not a single best hardware configuration but a set of optimal ones achieving different trade-offs between cost and runtime. In practice, some choices may be overly costly or take days to train.
  • ACL-IJCNLP 2021 Workshop on e-Commerce and NLP (ECNLP)
    2021
    Large-scale unsupervised abstractive summarization is sorely needed to automatically scan millions of customer reviews in today’s fast-paced e-commerce landscape. We address a key challenge in unsupervised abstractive summarization – reducing generic and uninformative content and producing useful information that relates to specific product aspects. To do so, we propose to model reviews in the context of
  • Sareh Nabi, Houssam Nassif, Joseph Hong, Hamed Mamani, Guido Imbens
    The Journal of Management Science
    2021
    Adding domain knowledge to a learning system is known to improve results. In multi-parameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, the various model parameters can have different learning rates in real-world problems, especially with skewed data. Two often-faced challenges in Operation Management and Management Science applications are the absence of informative
  • Interspeech 2021
    2021
    Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech. This becomes a bottleneck for training robust models for accented speech which typically contains high variability in pronunciation and other semantics, since obtaining large amounts of annotated accented data is both tedious and costly. Often, we only have access to large amounts of unannotated
  • Interspeech 2021
    2021
    Speaker diarization accuracy can be affected by both acoustics and conversation characteristics. Determining the cause of diarization errors is difficult because speaker voice acoustics and conversation structure co-vary, and the interactions between acoustics, conversational structure, and diarization accuracy are complex. This paper proposes a methodology that can distinguish independent marginal effects
  • Journal of Causal Inference
    2021
    The Principle of Insufficient Reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The Maximum Entropy Principle (MaxEnt) generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause
  • Francois-Xavier Aubet, Daniel Zūgner, Jan Gasthaus
    ICML 2021 Time Series Workshop
    2021
    Time series data are often corrupted by outliers or other kinds of anomalies. Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving performance of other time series tasks (e.g. forecasting). Recent deep-learning-based approaches to anomaly detection and forecasting commonly assume that the proportion of anomalies in the training data is small enough to ignore
  • Richard Kurle, Tim Januschowski, Jan Gasthaus, Yuyang (Bernie) Wang
    NeurIPS 2021 Workshop on Bayesian Deep Learning
    2021
    Probabilistic inference of Neural Network parameters is challenging due to the highly multi-modal likelihood functions. Most importantly, the permutation invariance of the neurons of the hidden layers renders the likelihood function unidentifiable with a factorial number of equivalent (symmetric) modes, independent of the data. We show that variational Bayesian methods that approximate the (multi-modal)
  • Shreyansh Bhatt, Jinjin Zhao, Candace Thille, Dawn Zimmaro, Neelesh Gattani
    ACM L@S 2020
    2020
    Open navigation online learning systems allow learners to choose the next learning activity. These systems can be instrumented to provide learners with feedback to help them choose the next learning activity. One type of feedback is providing an estimate of the learner’s current skill proficiency. A learner can then choose to skip the remaining learning activities for that skill after achieving proficiency
  • Gaurush Hiranandani, Sumeet Katariya, Nikhil Rao, Karthik Subbian
    NeurIPS 2019 Workshop on Bayesian Learning
    2019
    Search engine performance is usually good on head (high-frequency) queries due to the rich availability of historical behavioral signals on these queries. An over-reliance on past behavioral signals can potentially impact the performance on the tail (low-frequency) queries, where there is a lack of behavioral data. One way to address this issue is to reformulate a tail query into an appropriate head query
RO, Iasi
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.
EE, Tallinn
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.
GB, London
Are you a MS student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for a customer obsessed Data Scientist Intern who can innovate in a business environment, building and deploying machine learning models to drive step-change innovation and scale it to the EU/worldwide. If this describes you, come and join our Data Science teams at Amazon for an exciting internship opportunity. If you are insatiably curious and always want to learn more, then you’ve come to the right place. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science Key job responsibilities As a Data Science Intern, you will have following key job responsibilities: • Work closely with scientists and engineers to architect and develop new algorithms to implement scientific solutions for Amazon problems. • Work on an interdisciplinary team on customer-obsessed research • Experience Amazon's customer-focused culture • Create and Deliver Machine Learning projects that can be quickly applied starting locally and scaled to EU/worldwide • Build and deploy Machine Learning models using large data-sets and cloud technology. • Create and share with audiences of varying levels technical papers and presentations • Define metrics and design algorithms to estimate customer satisfaction and engagement A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, France, Germany, Ireland, Israel, Italy, Luxembourg, Netherlands, Poland, Romania, Spain and the UK). Please note these are not remote internships.
IL, Tel Aviv
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note these are not remote internships.
GB, London
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.
US, WA, Seattle
Passionate about books? The Amazon Books personalization team is looking for a talented Applied Scientist II to help develop and implement innovative science solutions to make it easier for millions of customers to find the next book they will love. In this role you will: - Collaborate within a dynamic team of scientists, economists, engineers, analysts, and business partners. - Utilize Amazon's large-scale computing and data resources to analyze customer behavior and product relationships. - Contribute to building and maintaining recommendation models, and assist in running A/B tests on the retail website. - Help develop and implement solutions to improve Amazon's recommendation systems. Key job responsibilities The role involves working with recommender systems that combine Natural Language Processing (NLP), Reinforcement Learning (RL), graph networks, and deep learning to help customers discover their next great read. You will assist in developing recommendation model pipelines, analyze deep learning-based recommendation models, and collaborate with engineering and product teams to improve customer-facing recommendations. As part of the team, you will learn and contribute across these technical areas while developing your skills in the recommendation systems space. A day in the life In your day-to-day role, you will contribute to the development and maintenance of recommendation models, support the implementation of A/B test experiments, and work alongside engineers, product teams, and other scientists to help deploy machine learning solutions to production. You will gain hands-on experience with our recommendation systems while working under the guidance of senior scientists. About the team We are Books Personalization a collaborative group of 5-7 scientists, 2 product leaders, and 2 engineering teams that aims to help find the right next read for customers through high quality personalized book recommendation experiences. Books Personalization is a part of the Books Content Demand organization, which focuses on surfacing the best books for customers wherever they are in their current book journey.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques
CA, ON, Toronto
Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer. As a Principal Applied Scientist, you will work with talented peers pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors. This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work at scale. This position requires experience with developing Computer Vision, Multi-modal LLMs and/or Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms. Key job responsibilities - You will be responsible for defining key research directions in Multimodal LLMs and Computer Vision, adopting or inventing new techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. - You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. - You will also participate in organizational planning, hiring, mentorship and leadership development. - You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).
DE, BE, Berlin
Are you interested in enhancing Alexa user experiences through Large Language Models? The Alexa AI Berlin team is looking for an Applied Scientist to join our innovative team working on Large Language Models (LLMs), Natural Language Processing, and Machine/Deep Learning. You will be at the center of Alexa's LLM transformation, collaborating with a diverse team of applied and research scientists to enhance existing features and explore new possibilities with LLMs. In this role, you'll work cross-functionally with science, product, and engineering leaders to shape the future of Alexa. Key job responsibilities As an Applied Scientist in Alexa Science team: - You will develop core LLM technologies including supervised fine tuning and prompt optimization to enable innovative Alexa use cases - You will research and design novel metrics and evaluation methods to measure and improve AI performance - You will create automated, multi-step processes using AI agents and LLMs to solve complex problems - You will communicate effectively with leadership and collaborate with colleagues from science, engineering, and business backgrounds - You will participate in on-call rotations to support our systems and ensure continuous service availability A day in the life As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create technical roadmaps and drive production level projects that will support Amazon Science. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. About the team You would be part of the Alexa Science Team where you would be collaborating with Fellow Applied and research scientists!
US, WA, Redmond
Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and under-served communities around the world. We are looking for an accomplished Applied Scientist who will deliver science applications such as anomaly detection, advanced calibration methods, space engineering simulations, and performance analytics -- to name a few. Key job responsibilities • Translate ambiguous problems into well defined mathematical problems • Prototype, test, and implement state-of-the-art algorithms for antenna pointing calibration, anomaly detection, predictive failure models, and ground terminal performance evaluation • Provide actionable recommendations for system design/definition by defining, running, and summarizing physically-accurate simulations of ground terminal functionality • Collaborate closely with engineers to deploy performant, scalable, and maintainable applications in the cloud Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. A day in the life In this role as an Applied Scientist, you will design, implement, optimize, and operate systems critical to the uptime and performance of Kuiper ground terminals. Your contributions will have a direct impact on customers around the world. About the team This role will be part of the Ground Software & Analytics team, part of Ground Systems Engineering. Our team is responsible for: • Design, development, deployment, and support of a Tier-1 Monitoring and Remediation System (MARS) needed to maintain high availability of hundreds of ground terminals deployed around the world • Ground systems integration/test (I&T) automation • Ground terminal configuration, provisioning, and acceptance automation • Systems analysis • Algorithm development (pointing/tracking/calibration/monitoring) • Software interface definition for supplier-provided hardware and development of software test automation