The most widely used technology to identify the proteins present in a complex biological sample istandem mass spectrometry,which quickly produces alarge collection of spectra representative of thepeptides (i.e., protein subsequences) present in the original sample.

Basedontheseprinciples, weproposethe Distributed Morphological Attention Policy (DMAP) architecture (Figure 1). Weproposea Distributed Morphological Policy (DMAP) toaddressthisproblem (Figure 1).

Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost.

We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs.

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

This paper explores the problem of building ML systems that failloudly, investigating methods for detecting dataset shift, identifying exemplarsthat most typify the shift, and quantifying shift malignancy. We focus on severaldatasets and various perturbations to both covariates and label distributions withvarying magnitudes and fractions of data affected. Interestingly, we show thatacross the dataset shifts that we explore, a two-sample-testing-based approach,using pre-trained classifiers for dimensionality reduction, performs best.