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Maybe it's just me misunderstanding, but to me "inference" and "training" are one and the same. But the article defined it thus: This first generation of TPUs

by pc2g4d 10y ago

Maybe it's just me misunderstanding, but to me "inference" and "training" are one and the same. But the article defined it thus: This first generation of TPUs targeted inference (the use of an already trained model, as opposed to the training phase of a model, which has somewhat different characteristics) This Nvidia article treats them differently, too: https://blogs.nvidia.com/blog/2016/08/22/difference-deep-learning-training-inference-ai/ https://blogs.nvidia.com/blog/2016/08/22/difference-deep-lea... But the definition of "statistical inference" on Wikipedia says "Statistical inference is the process of deducing properties of an underlying distribution by analysis of data" which seems exactly like training.

mjn 10y ago
Some parts of stats (esp. classically) do use "inference" for the whole process of going from data -> result, especially when doing descriptive rather than predictive statistics. In most of ML the process is split into two phases, training a model on a data set, followed by using the trained model to predict labels (or whatever else it's predicting) for new data. Training is an implementation of statistical induction, from data to model, while model "use" or "evaluation" is an implementation of (probabilistic) deduction, from model + query to result. In ML, "inference" is usually a synonym for the "deduction" or "evaluation" portion. It makes sense to me intuitively if you think of it as learning things vs. inferring things from learned knowledge. But you do find constructions using it as a synonym for training too, as in phrases like "model inference" (which means inferring models from data, aka model induction or training). Inferring things from other things is a pretty general concept, so it can be slippery without context...
DanWaterworth 10y ago
Inference in this context means getting the output of a neural network on some input. Training meaning adjusting a neural network to so that it's outputs are more like the training data. Training involves finding derivatives and requires higher precision calculations which is why they don't train using TPUs.
kevinnk 10y ago
Training is inference, except over model space instead of possible outputs. Depending on what models you're looking at, the two can have very different computational properties, so treating them separately is not surprising even if what they're doing is conceptually similar.
deleted 10y ago
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by pc2g4d 10y ago

mjn 10y ago

Some parts of stats (esp. classically) do use "inference" for the whole process of going from data -> result, especially when doing descriptive rather than predictive statistics. In most of ML the process is split into two phases, training a model on a data set, followed by using the trained model to predict labels (or whatever else it's predicting) for new data. Training is an implementation of statistical induction, from data to model, while model "use" or "evaluation" is an implementation of (probabilistic) deduction, from model + query to result. In ML, "inference" is usually a synonym for the "deduction" or "evaluation" portion. It makes sense to me intuitively if you think of it as learning things vs. inferring things from learned knowledge. But you do find constructions using it as a synonym for training too, as in phrases like "model inference" (which means inferring models from data, aka model induction or training). Inferring things from other things is a pretty general concept, so it can be slippery without context...

DanWaterworth 10y ago

Inference in this context means getting the output of a neural network on some input. Training meaning adjusting a neural network to so that it's outputs are more like the training data. Training involves finding derivatives and requires higher precision calculations which is why they don't train using TPUs.

kevinnk 10y ago

Training is inference, except over model space instead of possible outputs. Depending on what models you're looking at, the two can have very different computational properties, so treating them separately is not surprising even if what they're doing is conceptually similar.

deleted 10y ago

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