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If you're working in a domain, is it really that much to ask to become familiar with it? Especially if the domain has a large theoretical component. When we te

If you're working in a domain, is it really that much to ask to become familiar with it? Especially if the domain has a large theoretical component. When we teach people software engineering we teach them concepts like "give your variables meaningful names". Now that we're in sub-domain of implementing some mathematics in software, I'd argue that matching the variables and functions to their source (more or less) _is_ exactly "giving your variables meaningful names". > A great deal of complexity could be removed from machine learning papers by notating algorithms as algorithms rather than formulae And you would immediately lose the ability to quickly and easily recognise similar patterns and abstractions that mathematical notation so fluently allows.

zaphirplane 8y ago
You have hit the nail on the head meaningful names != long names
YeGoblynQueenne 8y ago
>> If you're working in a domain, is it really that much to ask to become familiar with it? Especially if the domain has a large theoretical component. If the domain has a large theoretical component. Here, we're talking about statistical machine learning and neural networks in particular, where this is, for the most part, not at all the case. >> And you would immediately lose the ability to quickly and easily recognise similar patterns and abstractions that mathematical notation so fluently allows. I disagree. An algorithm is mathematical notation, complete with immediately recognisalbe patterns and abstractions (for-loops, conditional blocks, etc). And, btw, so is computer code: it is formal notation that, contrary to mathematical formulate that require some familiarity with the conventions of a field to read and understand, has an objective interpretation- in the form of a compiler for the language used. So machine learning papers could very well notate their algorithm in Python, even a high-level abstraction (without all the boilerplate) of the algorithm, and that would actually make them much more accessible to a larger number of people. Mathematical notation, as in formulae, is not required- it's a tradition in the field, but that's all. However, that's a bit of a digression from the subject of the naming of variables. Apologies. It's still relevant to the compreshensibility of formal notation.

zaphirplane 8y ago

You have hit the nail on the head meaningful names != long names

YeGoblynQueenne 8y ago

>> If you're working in a domain, is it really that much to ask to become familiar with it? Especially if the domain has a large theoretical component. If the domain has a large theoretical component. Here, we're talking about statistical machine learning and neural networks in particular, where this is, for the most part, not at all the case. >> And you would immediately lose the ability to quickly and easily recognise similar patterns and abstractions that mathematical notation so fluently allows. I disagree. An algorithm is mathematical notation, complete with immediately recognisalbe patterns and abstractions (for-loops, conditional blocks, etc). And, btw, so is computer code: it is formal notation that, contrary to mathematical formulate that require some familiarity with the conventions of a field to read and understand, has an objective interpretation- in the form of a compiler for the language used. So machine learning papers could very well notate their algorithm in Python, even a high-level abstraction (without all the boilerplate) of the algorithm, and that would actually make them much more accessible to a larger number of people. Mathematical notation, as in formulae, is not required- it's a tradition in the field, but that's all. However, that's a bit of a digression from the subject of the naming of variables. Apologies. It's still relevant to the compreshensibility of formal notation.