Customer-obsessed science
-
May 17, 2024A novel loss function and a way to aggregate multimodal input data are key to dramatic improvements on some test data.
-
May 10, 2024Using large language models to discern commonsense relationships can improve performance on downstream tasks by as much as 60%.
-
April 30, 2024Using causal random forests and Bayesian structural time series to extrapolate from sparse data ensures that customers get the most useful information as soon as possible.
-
-
May 20 - 25, 2024
-
June 9 - 14, 2024
-
June 16 - 21, 2024
-
March 18, 2024
Tokenizing time series data and treating it like a language enables a model whose zero-shot performance matches or exceeds that of purpose-built models. Update: Amazon scientists how now released the training code for Chronos, which is available on GitHub.
-
ACM FAccT 20242024We present a broad characterization of gender representation in a large heterogeneous sample of retail products. In particular, we study online product textual information, such as titles and descriptions. Our goal is to understand from a semantic perspective, differences and similarities in how girls (women) and boys (men) are represented. We perform a comparative analysis of the language used in gendered
-
SIGIR 20242024Sequential recommendation systems suggest products based on users’ historical behaviours. The inherent sparsity of user-item interactions in a vast product space often leads to unreliable recommendations. Recent research addresses this challenge by leveraging auxiliary product relations to mitigate recommendation uncertainty, and quantifying uncertainty in recommendation scores to modify the candidates
-
NAACL 2024 Workshop on TrustNLP2024Language models, pre-trained on large amounts of unmoderated content, have been shown to contain societal biases. Mitigating such biases typically requires access to model parameters and training schemas. In this work, we address bias mitigation at inference time, such that it can be applied to any black-box model. To this end, we propose a belief generation and aug-mentation framework, BELIEVE, that demonstrates
News and features
-
April 26, 2024Awardees, who represent 51 universities in 15 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.
-
April 09, 2024How the team behind Echo Frames delivered longer battery life and improved sound quality inside the slim form factor of a pair of eyeglasses.
-
March 21, 2024The principal economist and his team address unique challenges using techniques at the intersection of microeconomics, statistics, and machine learning.