Customer-obsessed science

Localizing defects in products is a critical component of industrial pipelines in manufacturing, retail, and many other industries to ensure consistent delivery of the highest quality products. Automated anomaly localization systems leveraging computer vision have the potential to replace laborious and subjective manual inspection of products. Recently, there have been tremendous efforts in the domain of

Self-supervised pretraining has advanced the capabilities of many computer vision tasks without requiring additional labels. One drawback is this technique requires extensive datasets and computational resources. This requirement of large datasets to pretrain with has often precluded the use of smaller, more niche datasets. Recently a method of pretraining has been developed that uses several stages of

Although black-box models can accurately predict outcomes such as weather patterns, they often lack transparency, making it challenging to extract meaningful insights (such as which atmospheric conditions signal future rainfall). Model explanations attempt to identify the essential features of a model, but these explanations can be inconsistent: two near-optimal models may admit vastly different explanations

User modeling is of great importance in personalization services. Many existing methods treat users as interaction sequences to capture users’ evolving interests. Another line of research models each user as a user graph in which the users’ interactions are modeled as nodes. Nodes (interactions) in user graphs are connected via edges that reflect certain relations such as item similarity. The graph-based

In this work, we investigate the possibility of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model that learns the underlying task distribution and combine Thompson sampling