

-
September 29, 2023From classic problems like image segmentation and object detection to theoretical topics like data representation and “machine unlearning”, Amazon researchers’ ICCV papers showcase the diversity of their work in computer vision.
-
September 26, 2023Time series forecasting enables up-to-the-minute trend recognition, while novel two-step training process improves forecast accuracy.
-
September 20, 2023Leveraging large language models will make interactions with Alexa more natural and engaging.
-
-
October 2 - 6, 2023
-
October 15 - 18, 2023
-
October 02, 2023Two PhD students and five professors will receive funding to conduct research toward improving the robustness and efficiency of AI systems.
-
September 19, 2023The new Fulfillment by Amazon system empowers sellers to have more transparency and control over their capacity within Amazon’s fullfilment network by applying market-based principles.
-
August 28, 2023AWS service enables machine learning innovation on a robust foundation.
-
August 24, 2023Registrations for the third edition of the ML Summer School closed on Sept. 6.
-
ECML-PKDD 2023 Workshop on Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications (COLLM)2023Collection of annotated dialogs for training task-oriented dialog systems have been one of the key bottlenecks in improving current models. While dialog response generation has been widely studied on the agent side, it is not evident if similar generative models can be used to generate a large variety of, and often unexpected, user inputs that real dialog systems encounter in practice. Existing data augmentation
-
ECML PKDD 2023 International Workshop on Machine Learning for Irregular Time Series2023Demand forecasting is a prominent business use case that allows retailers to optimize inventory planning, logistics, and core business decisions. One of the key challenges in demand forecasting is accounting for relationships and interactions between articles. Most modern forecasting approaches provide independent article-level predictions that do not consider the impact of related articles. Recent research
-
Deep learning training compilers accelerate and achieve more resource-efficient training. We present a deep learning compiler for training consisting of three main features, a syncfree optimizer, compiler caching and multi-threaded execution. We demonstrate speedups for common language and vision problems against native and XLA baselines implemented in PyTorch.
-
PRML 20232023Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing
-
ECML PKDD 2023 International Workshop on Machine Learning for Irregular Time Series2023Mixup is a domain-agnostic approach for data augmentation, originally proposed for training Deep Neural Networks (DNNs) for image classification. It obtains additional data for training by sampling from linear interpolations of model inputs and their labels. While proven to be effective for computer vision (CV) and natural language processing (NLP) tasks, it remains unknown if mixup can bring performance
-
October 03, 2023Team TWIZ from NOVA School of Science and Technology awarded $500,000 prize for first-place overall performance.
-
September 21, 2023The submission period opens September 21 and closes on November 1.
-
September 12, 2023GauchoChat wins $250,000 first place prize in overall competition; Chirpy Cardinal earns $250,000 for first place in scientific innovation category.
Working at Amazon
View allMeet the people driving the innovation essential to being the world’s most customer-centric company.
View all
-
August 29, 2023Chamsi Hssaine and Hanzhang Qin, the inaugural postdoctoral scientists with the Supply Chain Optimization Technologies team, share what they learned from Amazon scientists.
-
August 24, 2023From the urgent challenge of "machine unlearning" to overcoming the problem of critical learning periods in deep neural networks, Alessandro Achille is tackling fundamental issues on behalf of Amazon customers.
-
August 21, 2023How Linghui Luo's research helps ensure code is checked and ready to deploy.