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Earlier quoted context omitted.
Your generic dataset idea is either crazy-brilliant, or I'm stupid: conceivably both. Either way, I'm struggling to understand how it might work. Care to say more?
The OP probably has something more in mind, but I'd imagine overfitting a small NN on a large dataset to a point of convergence and then shipping the small NN instead of the large dataset.
I came hoping to find something like neural radiance fields [1] but for generic datasets (ie. latent representations of data that allow you to store the 'data' without storing it explicitly row by row). ...but, this is: > What if, instead, data and queries can be represented as short natural language sentences, and queries can be answered from these sentences? Useful for some things, but not useful for most things. […
In that case the system doesn't have transparency and control. Imagine you are given a fact that "Alice works at Google" and 1 month later you want to remove that and add "Alice works at Facebook". In fully latent representation model of the data, you can not guarantee this.
For similar reasons, you can not guarantee preventing implicit biases in the data. Pre-trained generative models tend to hallucinate facts, for example you have two facts: "Alice works at hospital", "Bob works at hospital", if someone asks what does Alice/Bob do? You don't want your model hallucinates Nurse/Doctor.
I came hoping to find something like neural radiance fields [1] but for generic datasets (ie. latent representations of data that allow you to store the 'data' without storing it explicitly row by row). ...but, this is: > What if, instead, data and queries can be represented as short natural language sentences, and queries can be answered from these sentences? Useful for some things, but not useful for most things. […
I came hoping to find something like neural radiance fields [1] but for generic datasets (ie. latent representations of data that allow you to store the 'data' without storing it explicitly row by row). ...but, this is: > What if, instead, data and queries can be represented as short natural language sentences, and queries can be answered from these sentences? Useful for some things, but not useful for most things. […
I came hoping to find something like neural radiance fields [1] but for generic datasets (ie. latent representations of data that allow you to store the 'data' without storing it explicitly row by row). ...but, this is: > What if, instead, data and queries can be represented as short natural language sentences, and queries can be answered from these sentences? Useful for some things, but not useful for most things. […
Since there are sibling comments wondering what this means, here’s a perspective: A database (simplest case is an array or a dict) isn’t fundamentally different from a function in its interface. To both of them you submit a query/argument and they return some value. A “function” might “compute” the value after the query is submitted, while the database might “store” a pre-computed value. Especially in physical contex…
I came hoping to find something like neural radiance fields [1] but for generic datasets (ie. latent representations of data that allow you to store the 'data' without storing it explicitly row by row). ...but, this is: > What if, instead, data and queries can be represented as short natural language sentences, and queries can be answered from these sentences? Useful for some things, but not useful for most things. […
How is it possible that the original submission has been on the front page for 8+ hours, and all discussion is focused on this completely unrelated link?
Have people stopped reading original submission links in favor of comments so much that the discussion is no longer related to the original submission at all?
I have thought that cost-based optimizers in database query planning could definitely overlap with neural network approaches. I know there are some papers in the area, but I haven't seen any implementations.