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Found 6 papers, 1 papers with code
Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework controlling FDR (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple-hypothesis testing.
Leveraging Unlabeled Data for Sketch-based Understanding
Sketch-based understanding is a critical component of human cognitive learning and is a primitive communication means between humans.
Scalable Visual Attribute Extraction through Hidden Layers of a Residual ConvNet
Visual attributes play an essential role in real applications based on image retrieval.
NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy
Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science.
Cross-Modality Personalization for Retrieval
We incorporate constraints that encourage gaze and caption samples on the same image to be close in a learned space; we refer to this as content modeling.
Image Retrieval with Mixed Initiative and Multimodal Feedback
How would you search for a unique, fashionable shoe that a friend wore and you want to buy, but you didn't take a picture?
