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Found 4 papers, 1 papers with code
Robust Transfer Learning with Unreliable Source Data
This paper addresses challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals between the target and source distribution.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations.
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