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Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data.

Different variants ofmomentum, including heavyball momentum, Nesterov's accelerated gradient (NAG), and quasi-hyperbolic momentum (QHM), havedemonstrated success onvarious tasks. Our results are most closely related to the work of Mandt et al.[19]who use stationaryanalysis of SGD with momentum to perform approximateBayesianinference.

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We perform finetuning with image-textcontrastiveand image-textmatching losses. During inference, VLMO is first used as a dual encoder to obtain top-k candidates, then the model is used as a fusionencoder torerankthecandidates. For the text-only pre-training data, we use English Wikipedia and BookCorpus [5]. Table 1: Ablation study of the shared self-attention module used in Multiway Transformer.

PROSPECT provides value to proteomics and machine learning researchers by including several high-quality annotations and by being accessible in terms of format and structure for applying machinelearning.

This stands in sharp contrast to fully connected networks, which are not universal approximators if their width is the input dimensiond[21,11].