Neural Databases
arxiv.org
Neural Databases
1–10 of 32 posts
Re: Neural Databases
#2Surely a link to arXiv rather than a tweet would be better?
Re: Neural Databases
#3Surely a link to arXiv rather than a tweet would be better? https://arxiv.org/abs/2010.06973
Tweets provide some nice context by the author as commentary.
Re: Neural Databases
#4I 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.
[1] - eg. https://www.matthewtancik.com/nerf
Re: Neural Databases
#5I 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. […
Thanks for the nerf link - nice to start a day with something this impressive!
Re: Neural Databases
#6[deleted]
Re: Neural Databases
#7I 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. […
Wow, that seems way beyond what I would have expected the state of the art to be. Iiuc they're generating arbitrary perspectives of geometry from one or two photos, right?
Re: Neural Databases
#8Re: Neural Databases
#9I 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. […
Wow, that seems way beyond what I would have expected the state of the art to be. Iiuc they're generating arbitrary perspectives of geometry from one or two photos, right?
Not one or two, more like one or two hundred. But otherwise: yeah.
Re: Neural Databases
#10I 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. […
Wow, that seems way beyond what I would have expected the state of the art to be. Iiuc they're generating arbitrary perspectives of geometry from one or two photos, right?
Have a look at the google drive files if you click on the data link; it's a lot more than 2 photos.
eg. That dinosaur skeleton is derived from 60 photos. The drumkit comes from ~100.
...so it's not magic, it's very close to what you get from standard photogrammetry. The big part of this is that it isn't representing the scene as block of voxels like some other approaches.
> The biggest practical tradeoffs between these methods are time versus space.
> LLFF produces a large 3D voxel grid for every input image, resulting in enormous storage requirements (over 15GB for one “Realistic Synthetic” scene).
> Our method requires only 5 MB for the network weights (a relative compression of 3000× compared to LLFF), which is even less memory than the input images alone for a single scene from any of our datasets.
Anyway, so... if you could do the same sort of thing with a similar accuracy to non-images for a 'neural representation database', that'd be pretty neat.