WebFeb 24, 2024 · This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode …

Workshop on Computational Efficiency and High-Dimensional …

WebJul 15, 2014 · I have worked on efficient strategies to build and vend robust and transferrable representations using techniques such as transfer learning, multi-task learning, knowledge distillation, etc ... WebExtensive experiments demonstrate that even without access to labels and tasks, our model is still able to enhance robustness against adversarial attacks on three downstream tasks (node classification, link prediction, and community detection) by an average of +16.5% compared with existing methods. Topics: AAAI firecracker redhead line dance

Hyperspectral anomaly detection using ensemble and robust …

Weberature on unsupervised representation learning, across a variety of modalities. First, scale mat-ters: good representation learning requires a large amount of data. Second, unsupervised represen-tations consistently improve robustness on down-stream tasks. And finally, representations learned from multilingual data can transfer across many ... WebApr 14, 2024 · To enable efficient and robust similarity computation on massive-scale trajectories, we developed a novel RSTS model based on deep representation learning, in which we take the time components ... WebMar 3, 2024 · Despite the great success of deep neural networks for representation learning [He2015, he2024moco], it heavily relies on collecting large-scale training data samples, which turns out to be non-trivial in real-world applications.Therefore, how to form robust deep representation learning under the data scarcity by exploring the sample … esther quevedo