Comments (3)
what's the algorithm of Adas? Seems no document is provided.
from adabelief-optimizer.
It seems that it have description only in the repo inside "Theory" and "How ADAS works" sections...
from adabelief-optimizer.
Iām not quite sure by just looking at these sections. It seems the general idea is to perform gradient descent on per-element lr. Seems to be interesting. But Iām quite concerned about the fast convergence is due to lr is rapidly decayed, rather than it truly learns well. Another concern is with computation, because ADAS needs to take extra gradient w.r.t learning rate, not sure how much burden will it cost. Perhaps need some more validation.
from adabelief-optimizer.
Related Issues (20)
- fine-tune with bert models HOT 2
- Please add a license HOT 1
- MSVAG HOT 1
- Why does g_t substract m_t, instead of m_{t-1} ? HOT 1
- On imagenet accuracy result 70.08 HOT 1
- Documentation (at least for TF) and weight_decouple is not an option HOT 2
- FileNotFoundError for ImageNet HOT 1
- Changing init learning rate HOT 2
- Question about SGD optimizer in LSTM experiments HOT 1
- Compatibility with warmup HOT 2
- Inconsistent computation of weight_decay and grad_residual among pytorch versions HOT 5
- Your method is just equivalent to SGD with a changable global learning rate. HOT 3
- Some questions related to import adabelief HOT 2
- Tensorflow restoration issue HOT 1
- weight_decouple in adabelief tf HOT 1
- Inconsistent use of epsilon HOT 4
- Suppressing weight decoupling and rectification messages HOT 1
- The problem of reproducing the result of ImageNet HOT 4
- AttributeError: 'AdaBeliefOptimizer' object has no attribute '_set_hyper' HOT 4
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