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_d0s_ t1_j7g250y wrote
Reply to High-speed cameras and deep learning [Research] by A15L
Not the same, but I'd suggest to look into event cameras
_d0s_ t1_ivxk6y9 wrote
Reply to [D]Transformers! by No_Captain_856
It's also used for spatial embedding of patches in an image
Besides the positional embedding transforms also use the attention mechanism which can be beneficial for some problems on its own
_d0s_ t1_ivp036a wrote
Reply to [D] Is there an advantage in learning when taking the average Gradient compared to the Gradient of just one point by CPOOCPOS
besides being extremely computationally expensive, how would one define the size of the volume? it's similar to the problem of defining a step size which oversteps when too large or takes forever when too small. likewise defining a very small volume might get us caught in local minima.
i guess this thought is similar to smoothing like another poster mentioned.
_d0s_ t1_j8d1psc wrote
Reply to [D] Is a non-SOTA paper still good to publish if it has an interesting method that does have strong improvements over baselines (read text for more context)? Are there good examples of this kind of work being published? by orangelord234
YES! improvement is not only to create the best models but also how you get there. for example, you could argue that your approach is much more computationally efficient.