Gradient Noise
Definition
Gradient noise refers to random fluctuations in training gradients; here, deliberately added noise is explained.
When deliberately adding gradient noise, the gradient calculated during training is altered by a random term. Neelakantan and other authors investigate time-dependent Gaussian noise whose variance decreases during training. This entry refers to this method.
Simply explained
Like small random directional changes during a search that can get you out of an unfavorable track. However, a neural network searches in a mathematical parameter space and does not wander through a landscape.
Why it matters in practice
In own training experiments, distinguishing between built-in randomness and additionally applied noise is relevant. The work treats the additional noise as a variable part of training.
Example from business
Possible example: An AI team compares multiple training runs of a small network with and without additional gradient noise.
Limits and pitfalls
- Additional noise does not improve every setup. The work also reports experiments without improvement with suitable initialization or other settings. A conspicuous single improvement is therefore not a general promise of effect.
Origin & history
The cited original version by Neelakantan and other authors was submitted on November 21, 2015. It refers to older related methods; the date is not considered the origin of all gradient noise.
Use in mid-sized companies
- Possible applications include training studies on initialization, robustness, and controlled varied optimization settings.
Frequently asked questions
Does the noise level remain constant in the described method?
No; the work investigates a time-dependent course with decreasing variance.
Is additional noise identical to the randomness of stochastic gradient descent?
The work explicitly distinguishes both.
Sources
- Adding Gradient Noise Improves Learning for Very Deep Networks · Neelakantan et al. (2015): Adding Gradient Noise Improves Learning for Very Deep Networks
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