Challenges in deploying machine learning: a survey of case studies
In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …
to a field capable of solving real-world business problems. However, the deployment of …
Effective conditioned and composed image retrieval combining clip-based features
Conditioned and composed image retrieval extend CBIR systems by combining a query
image with an additional text that expresses the intent of the user, describing additional …
image with an additional text that expresses the intent of the user, describing additional …
Towards universal image embeddings: A large-scale dataset and challenge for generic image representations
Fine-grained and instance-level recognition methods are commonly trained and evaluated
on specific domains, in a model per domain scenario. Such an approach, however, is …
on specific domains, in a model per domain scenario. Such an approach, however, is …
Itemsage: Learning product embeddings for shop** recommendations at pinterest
Learned embeddings for products are an important building block for web-scale e-
commerce recommendation systems. At Pinterest, we build a single set of product …
commerce recommendation systems. At Pinterest, we build a single set of product …
Olio: A Semantic Search Interface for Data Repositories
Search and information retrieval systems are becoming more expressive in interpreting user
queries beyond the traditional weighted bag-of-words model of document retrieval. For …
queries beyond the traditional weighted bag-of-words model of document retrieval. For …
Improving reproducibility of data science pipelines through transparent provenance capture
Data science has become prevalent in a large variety of domains. Inherent in its practice is
an exploratory, probing, and fact finding journey, which consists of the assembly, adaptation …
an exploratory, probing, and fact finding journey, which consists of the assembly, adaptation …
Billion-scale pretraining with vision transformers for multi-task visual representations
Large-scale pretraining of visual representations has led to state-of-the-art performance on a
range of benchmark computer vision tasks, yet the benefits of these techniques at extreme …
range of benchmark computer vision tasks, yet the benefits of these techniques at extreme …
Preference prediction based on a photo gallery analysis with scene recognition and object detection
In this paper, a user modeling task is examined by processing mobile device gallery of
photos and videos. We propose a novel engine for preferences prediction based on scene …
photos and videos. We propose a novel engine for preferences prediction based on scene …
GrokNet: Unified computer vision model trunk and embeddings for commerce
In this paper, we present GrokNet, a deployed image recognition system for commerce
applications. GrokNet leverages a multi-task learning approach to train a single computer …
applications. GrokNet leverages a multi-task learning approach to train a single computer …
Zero-shot heterogeneous transfer learning from recommender systems to cold-start search retrieval
Many recent advances in neural information retrieval models, which predict top-K items
given a query, learn directly from a large training set of (query, item) pairs. However, they are …
given a query, learn directly from a large training set of (query, item) pairs. However, they are …