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].

Tobuildabettergeneralized and easy-to-use pruning method, we propose AutoPrune, which prunes the network through optimizing a set of trainable auxiliary parameters instead of original weights.

Neural Information Processing Systems http://nips.cc/

Large Language Models that are evaluated on multiple metrics.

Theexisting benchmarks arefocused onsupervised learning, andtothe best of our knowledge, there is none for unsupervised learning.