11 research outputs found
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Creative professional users musical relevance criteria
Although known item searching for music can be dealt with by searching metadata using existing text search techniques, human subjectivity and variability within the music itself make it very difficult to search for unknown items. This paper examines these problems within the context of text retrieval and music information retrieval. The focus is on ascertaining a relationship between music relevance criteria and those relating to relevance judgements in text retrieval. A data-rich collection of relevance judgements by creative professionals searching for unknown musical items to accompany moving images using real world queries is analysed. The participants in our observations are found to take a socio-cognitive approach and use a range of content and context based criteria. These criteria correlate strongly with those arising from previous text retrieval studies despite the many differences between music and text in their actual content
Typilus: Neural Type Hints
Type inference over partial contexts in dynamically typed languages is
challenging. In this work, we present a graph neural network model that
predicts types by probabilistically reasoning over a program's structure,
names, and patterns. The network uses deep similarity learning to learn a
TypeSpace -- a continuous relaxation of the discrete space of types -- and how
to embed the type properties of a symbol (i.e. identifier) into it.
Importantly, our model can employ one-shot learning to predict an open
vocabulary of types, including rare and user-defined ones. We realise our
approach in Typilus for Python that combines the TypeSpace with an optional
type checker. We show that Typilus accurately predicts types. Typilus
confidently predicts types for 70% of all annotatable symbols; when it predicts
a type, that type optionally type checks 95% of the time. Typilus can also find
incorrect type annotations; two important and popular open source libraries,
fairseq and allennlp, accepted our pull requests that fixed the annotation
errors Typilus discovered.Comment: Accepted to PLDI 202