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Found 7 papers, 2 papers with code
Training and Serving System of Foundation Models: A Comprehensive Survey
Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems.
SPOT! Revisiting Video-Language Models for Event Understanding
This raises a question: with such weak supervision, can video representation in video-language models gain the ability to distinguish even factual discrepancies in textual description and understand fine-grained events?
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
In this paper, we present a scalable and efficient paradigm to address data sparsity and cold-start issues in CDR, named CDR-Adapter, by decoupling the original recommendation model from the mapping function, without requiring re-engineering the network structure.
Hybrid CNN -Interpreter: Interpret local and global contexts for CNN-based Models
Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regulation during operation for acceptance and deployment of AI-assisted applications.
Predicting Blossom Date of Cherry Tree With Support Vector Machine and Recurrent Neural Network
Our project probes the relationship between temperatures and the blossom date of cherry trees.
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
SAR-ShipNet: SAR-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-resolution Feature Fusion
This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network.
